BULK IEEE PROJECTS IN MATLAB ,BULK IEEE PROJECTS, IEEE 2015-16 MATLAB PROJECTS IN CHENNAI, 2015-16...

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MATLAB PROJECTS 2015

Sno. Topic Abstract Year 1. MATLAB2015_01 Machine Learning-Based

Coding Unit Depth

Decisions for Flexible

Complexity Allocation in High Efficiency Video

Coding

In this paper, we propose a machine learning-based

fast coding unit (CU) depth decision method for High

Efficiency Video Coding (HEVC), which optimizes the

complexity allocation at CU level with given rate-distortion (RD) cost constraints. First, we analyze quad-

tree CU depth decision process in HEVC

and model it as a three-level of hierarchical binary

decision problem. Second, a flexible CU depth decision structure is presented, which allows the performances of

each CU depth decision be smoothly transferred between

the coding complexity and RD performance. Then, a

three-output joint classifier consists of multiple binary

classifiers with different parameters is designed to control the risk of false prediction. Finally, a sophisticated RD-

complexity model is derived to determine the optimal

parameters for the joint classifier, which is capable of

minimizing the complexity in each CU depth at given RD

degradation constraints. Comparative experiments over various sequences show that the proposed CU depth

decision algorithm can reduce the computational

complexity from 28.82% to 70.93%, and 51.45% on

average when compared with the original HEVC test

model.

2015

2. MATLAB2015_02 Distinguishing Local and

Global Edits for Their

Simultaneous Propagation in a Uniform Framework

In propagating edits for image editing, some edits are

intended to affect limited local regions, while others act

globally over the entire image. However, the ambiguity problem in propagating edits is not adequately addressed

in existing methods. Thus, tedious user input

requirements remain since the user must densely or

repeatedly input control samples to suppress ambiguity.

In this paper, we address this challenge to propagate edits suitably by marking edits for local or global

propagation and determining their reasonable propagation

scopes automatically. Thus, our approach avoids

propagation conflicts, effectively resolving the ambiguity

problem. With the reduction of ambiguity, our method allows fewer and less-precise control samples than

existing methods. Furthermore, we provide a uniform

framework to propagate local and global edits

simultaneously, helping the user to quickly obtain the

intended results with reduced labor. With our unified framework, the potentially ambiguous interaction

between local and global edits (evident

in existing methods that propagate these two edit types in

series) is resolved. We experimentally demonstrate the

effectiveness of our method compared with existing methods.

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3. MATLAB2015_03 Face Recognition Across

Non-Uniform Motion

Blur, Illumination, and Pose

Existing methods for performing face recognition

in the presence of blur are based on the convolution

model and cannot handle non-uniform blurring situations that frequently arise from tilts and rotations in hand-held

cameras. In this paper, we propose a methodology for

face recognition in the presence of space-varying motion

blur comprising of arbitrarily-shaped kernels. We model

the blurred face as a convex combination of geometrically transformed instances of the focused

gallery face, and show that the set of all images obtained

by non-uniformly blurring a given image forms a convex

set. We first propose a non uniform blur-robust algorithm

by making use of the assumption of a sparse camera trajectory in the camera motion space to build an energy

function with l1-norm constraint on the camera

motion. The framework is then extended to handle

illumination variations by exploiting the fact that the set

of all images obtained from a face image by non-uniform blurring and changing the illumination forms a bi-convex

set. Finally, we propose an elegant extension to also

account for variations in pose.

2015

4. MATLAB2015_04 Swarm Intelligence for

Detecting Interesting

Events

in Crowded Environments

This work focuses on detecting and localizing

anomalous events in videos of crowded scenes, i.e.

divergences from a dominant pattern. Both motion and

appearance information are considered, so as to robustly

distinguish different kinds of anomalies, for a wide range of scenarios. A newly introduced concept based on swarm

theory, Histograms of Oriented Swarms (HOS), is applied

to capture the dynamics of crowded environments. HOS,

together with the well known Histograms of Oriented Gradients (HOG), are combined to build a descriptor that

effectively characterizes each scene. These appearance

and motion features are only extracted within

spatiotemporal volumes of moving pixels to ensure

robustness to local noise, increase accuracy in the detection of local, non dominant anomalies, and achieve a

lower computational cost. Experiments on benchmark

datasets containing various situations with human crowds,

as well as on traffic data, led to results that

surpassed the current state of the art, confirming the method’s efficacy and generality. Finally, the experiments

show that our approach achieves significantly higher

accuracy, especially for pixel-level event detection

compared to State of the Art (SoA)

methods, at a low computational cost.

2015

5. MATLAB2015_05 Content-Based Image

Retrieval Using Features

Extracted From Halftoning-Based Block

Truncation Coding

This paper presents a technique for Content-Based

Image Retrieval (CBIR) by exploiting the advantage of

low complexity Ordered-Dither Block Truncation Coding (ODBTC) for the generation of image content descriptor.

In encoding step, ODBTC compresses an image block

into corresponding quantizers and bitmap image. Two

image features are proposed to index an image, namely

Color Co-occurrence Feature (CCF) and Bit Pattern Features (BPF), which are generated directly from

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ODBTC encoded data streams without performing the

decoding process. The CCF and BPF of an image are

simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook.

Experimental results show that the proposed method is

superior to the Block Truncation Coding (BTC) image

retrieval systems and the other former methods, and thus

prove that the ODBTC scheme is not only suited for image compression since of its simplicity, but also offers

a simple and effective descriptor to index images in CBIR

6. MATLAB2015_06 Approximation and

Compression with Sparse Orthonormal Transforms

We propose a new transform design method that

targets the generation of compression-optimized transforms for next-generation multimedia applications.

The fundamental idea behind transform compression is to

exploit regularity within signals such that redundancy is

minimized subject to a fidelity cost. Multimedia signals,

in particular images and video, are well known to contain a diverse set of localized structures, leading to many

different types of regularity and to non stationary signal

statistics. The proposed method designs sparse

orthonormal transforms (SOT) that automatically exploit

regularity over different signal structures and provides an adaptation method that determines the best representation

over localized regions. Unlike earlier work that is

motivated by linear approximation constructs and model-

based designs that are limited to specific types of

signal regularity, our work uses general nonlinear approximation ideas and a data-driven setup to

significantly broaden its reach. We show that our SOT

designs provide a safe and principled

extension of the Karhunen-Loeve transform (KLT) by reducing to the KLT on Gaussian processes and by

automatically exploiting non-Gaussian statistics to

significantly improve over the KLT on more general

processes. We provide an algebraic optimization

framework that generates optimized designs for any desired transform structure (multi-resolution, block,

lapped, etc.) with significantly better n-term

approximation performance. For each structure, we

propose a new prototype codec and test over a

database of images. Simulation results show consistent increase in compression and approximation performance

compared with conventional methods.

2015

7. MATLAB2015_07 High-Resolution Face Verification Using

Pore-Scale Facial Features

Face recognition methods, which usually represent face images using holistic or local facial features, rely

heavily on alignment. Their performances also suffer a

severe degradation under variations in expressions or

poses, especially when there is one gallery per subject

only. With the easy access to high resolution (HR) face images nowadays, some HR face databases

have recently been developed. However, few studies have

tackled the use of HR information for face recognition or

verification. In this paper, we propose a pose-invariant

face-verification method, which is robust to alignment errors, using the HR information based on pore-scale

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facial features. A new key point descriptor, namely, pore-

Principal Component Analysis (PCA)- Scale Invariant

Feature Transform (PPCASIFT)—adapted from PCA-SIFT—is devised for the extraction of a compact set

of distinctive pore-scale facial features. Having matched

the porescale features of two-face regions, an effective

robust-fitting scheme is proposed for the face-verification

task. Experiments show that, with one frontal-view gallery only per subject, our proposed method

outperforms a number of standard verification

methods, and can achieve excellent accuracy even the

faces are under large variations in expression and pose.

8. MATLAB2015_08 DERF: Distinctive

Efficient Robust Features

From the Biological Modeling of

the P Ganglion Cells

Studies in neuroscience and biological vision have

shown that the human retina has strong computational

power, and its information representation supports vision tasks on both ventral and dorsal pathways. In this paper, a

new local image descriptor, termed distinctive efficient

robust features (DERF), is derived by modeling the

response and distribution properties of the parvocellular-

projecting ganglion cells in the primate retina. DERF features exponential scale distribution,

exponential grid structure, and circularly symmetric

function difference of Gaussian (DoG) used as a

convolution kernel, all of which are consistent with the

characteristics of the ganglion cell array found in neurophysiology, anatomy, and biophysics. In addition,

a new explanation for local descriptor design is presented

from the perspective of wavelet tight frames. DoG is

naturally a wavelet, and the structure of the grid points array in our descriptor is closely related to the spatial

sampling of wavelets. The DoG wavelet itself forms a

frame, and when we modulate the parameters of our

descriptor to make the frame tighter, the performance of

the DERF descriptor improves accordingly. This is verified by designing a tight frame DoG, which leads to

much better performance. Extensive experiments

conducted in the image matching task on the multiview

stereo correspondence data set demonstrate that DERF

outperforms state of the art methods for both hand-crafted and learned descriptors, while remaining robust and being

much faster to compute.

2015

9. MATLAB2015_09 Blind Inpainting using ℓ0 and Total Variation

Regularization

In this paper, we address the problem of image reconstruction with missing pixels or corrupted with

impulse noise, when the locations of the corrupted pixels

are not known. A logarithmic transformation is applied to

convert the multiplication between the image and binary

mask into an additive problem. The image and mask terms are then estimated iteratively with total variation

regularization applied on the image, and ℓ0 regularization

on the mask term which imposes sparseness on the

support set of the missing pixels. The resulting

alternating minimization scheme simultaneously estimates the image and mask, in the same iterative

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process. The logarithmic transformation also allows the

method to be extended to the Rayleigh multiplicative and

Poisson observation models. The method can also be extended to impulse noise removal by relaxing

the regularizer from the ℓ0 norm to the ℓ1 norm.

Experimental results show that the proposed method can

deal with a larger fraction of missing pixels than two

phase methods which first estimate the mask and then reconstruct the image.

10. MATLAB2015_10 A Source-Channel Coding

Approach to Digital Image Protection and Self-

Recovery

Watermarking algorithms have been widely applied

to the field of image forensics recently. One of these very forensic applications is the protection of images against

tampering. For this purpose, we need to design a

watermarking algorithm fulfilling two purposes in case of

image tampering: 1) detecting the tampered area of the

received image and 2) recovering the lost information in the tampered zones. State-of-the-art techniques

accomplish these tasks using watermarks consisting of

check bits and reference bits. Check bits are used for

tampering detection, whereas reference bits carry

information about the whole image. The problem of recovering the lost reference bits still stands. This paper is

aimed at showing that having the tampering location

known, image tampering can be modeled

and dealt with as an erasure error. Therefore, an

appropriate design of channel code can protect the reference bits against tampering. In the present proposed

method, the total watermark bit-budget is dedicated to

three groups: 1) source encoder output bits; 2) channel

code parity bits; and 3) check bits. In watermark embedding phase, the original image is source coded and

the output bit stream is protected using appropriate

channel encoder. For image recovery, erasure locations

detected by check bits help channel erasure decoder to

retrieve the original source encoded image. Experimental results show that our proposed scheme significantly

outperforms recent techniques in terms of image quality

for both watermarked and recovered

image. The watermarked image quality gain is achieved

through spending less bit-budget on watermark, while image recovery quality is considerably improved as a

consequence of consistent performance of designed

source and channel codes.

2015

11. MATLAB2015_11 Structured Sparse Priors for

Image Classification

Model-based compressive sensing (CS) exploits the

structure inherent in sparse signals for the design of better

signal recovery algorithms. This information about

structure is often captured in the form of a prior on the

sparse coefficients, with the Laplacian being the most common such choice (leading to l1-norm minimization).

Recent work has exploited the discriminative capability

of sparse representations for image classification by

employing class-specific dictionaries in the CS

framework. Our contribution is a logical extension of these ideas into structured sparsity for classification. We

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introduce the notion of discriminative class-specific priors

in conjunction with class specific dictionaries,

specifically the spike-and-slab prior widely applied in Bayesian sparse regression. Significantly, the proposed

framework takes the burden off the demand for abundant

training image samples necessary for the success of

sparsity-based classification schemes. We demonstrate

this practical benefit of our approach in important applications,

such as face recognition and object categorization.

12. MATLAB2015_12 Video Tracking Using Learned Hierarchical

Features

In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we

offline learn features robust to diverse motion patterns

from auxiliary video sequences. The hierarchical features

are learned via a twolayer convolutional neural network.

Embedding the temporal slowness constraint in the stacked architecture makes the learned

features robust to complicated motion transformations,

which is important for visual object tracking. Then, given

a target video sequence, we propose a domain adaptation

module to online adapt the pre-learned features according to the specific target object. The adaptation is conducted

in both layers of the deep feature learning module so as to

include appearance information of the specific target

object. As a result, the learned hierarchical features can

be robust to both complicated motion transformations and appearance changes of target objects. We

integrate our feature learning algorithm into three

tracking

methods. Experimental results demonstrate that significant improvement can be achieved by using our

learned hierarchical features, especially on video

sequences with complicated motion transformations.

2015

13. MATLAB2015_13 A Global/Local Affinity

Graph for Image

Segmentation

Construction of a reliable graph capturing perceptual

grouping cues of an image is fundamental for

graph-cut based image segmentation methods. In this

paper, we propose a novel sparse global/local affinity

graph over superpixels of an input image to capture both short and long range grouping cues, thereby

enabling perceptual grouping laws, e.g., proximity,

similarity, continuity, to enter in action through a

suitable graph cut algorithm. Moreover, we also evaluate

three major visual features, namely color, texture and shape,for their effectiveness in perceptual

segmentation and propose a simple graph fusion scheme

to implement some recent findings from psychophysics

which suggest combining these visual features

with different emphases for perceptual grouping. Specifically, an input image is first oversegmented into

superpixels at different scales. We postulate a gravitation

law based on empirical observations and divide

superpixels adaptively into small, medium and large sized

sets. Global grouping is achieved using medium sized superpixels through a sparse representation of

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superpixels’ features by solving a `0-minimization

problem, thereby enabling continuity or propagation of

local smoothness over long range connections. Small and large sized superpixels are then used to achieve

local smoothness through an adjacent graph

in a given feature space, thus implementing perceptual

laws, e.g., similarity and proximity. Finally, a

bipartite graph is also introduced to enable propagation of grouping cues between superpixels of different

scales. Extensive experiments are carried out on the

Berkeley Segmentation Database in comparison with

several state of the art graph constructions.

14. MATLAB2015_14 A Database for Evaluating

No-Reference

Image Quality Assessment

Algorithms

This paper presents a new database, CID2013,

to address the issue of using no-reference (NR) image

quality assessment algorithms on images with multiple

distortions. Current NR algorithms struggle to handle

images with many concurrent distortion types, such as real photographic images captured by different digital

cameras. The database consists of six image sets; on

average, 30 subjects have evaluated 12–14 devices

depicting eight different scenes for a total of 79 different

cameras, 480 images, and 188 subjects (67% female). The subjective evaluation method was a hybrid absolute

category rating-pair comparison developed for the study

and presented in this paper. This method utilizes a

slideshow of all images within a scene to allow the test

images to work as references to each other. In addition to mean opinion score value, the images are also rated using

sharpness, graininess, lightness, and color saturation

scales. The CID2013 database contains images used

in the experiments with the full subjective data plus extensive background information from the subjects. The

database is madefreely available for the research

community.

2015

15. MATLAB2015_15 An Efficient MRF

Embedded Level Set

Method for

Image Segmentation

This paper presents a fast and robust level set

method for image segmentation. To enhance the

robustness against noise, we embed a Markov random

field (MRF) energy function to the conventional level set

energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages

them to fall into the same region. To obtain

a fast implementation of the MRF embedded level set

model, we explore algebraic multigrid (AMG) and sparse

field method (SFM) to increase the time step and decrease the computation domain, respectively. Both AMG and

SFM can be conducted in a parallel fashion, which

facilitates the processing of our method for big image

databases. By comparing the proposed fast and

robust level set method with the standard level set method and its popular variants on noisy synthetic images,

synthetic aperture radar (SAR) images, medical images

and natural images, we comprehensively demonstrate the

new method is robust against various kinds of noises.

Especially, the new level set method can segment an image of size 500 by 500 within three seconds on

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MATLAB R2010b installed in a computer with 3.30GHz

CPU and 4GB memory.

16. MATLAB2015_16 Weighted Guided Image

Filtering

It is known that local filtering-based edgepreserving

smoothing techniques suffer from halo artifacts.

In this paper, a weighted guided image filter (WGIF) is

introduced by incorporating an edge-aware weighting into an existing guided image filter (GIF) to address the

problem. The WGIF inherits advantages of both global

and local smoothing filters in the sense that: 1) the

complexity of the WGIF is O(N) for an image with N

pixels, which is same as the GIF and 2) the WGIF can avoid halo artifacts like the existing global smoothing

filters. The WGIF is applied for single image detail

enhancement, single image haze removal, and fusion of

differently exposed images. Experimental results show

that the resultant algorithms produce images with better visual quality and at the same time halo artifacts can be

reduced/avoided from appearing in the final images with

negligible increment on running times.

2015

17. MATLAB2015_17 Distinctive Efficient

Robust Features From

the Biological Modeling of

the P Ganglion Cells

Studies in neuroscience and biological vision have

shown that the human retina has strong computational

power, and its information representation supports vision

tasks on both ventral and dorsal pathways. In this paper, a new local image descriptor, termed distinctive efficient

robust features (DERF), is derived by modeling the

response and distribution properties of the parvocellular-

projecting ganglion cells in the primate retina. DERF

features exponential scale distribution, exponential grid structure, and circularly symmetric function difference of

Gaussian (DoG) used as a convolution kernel, all of

which are consistent with the characteristics of the

ganglion cell array found in neurophysiology, anatomy,

and biophysics. In addition, a new explanation for local descriptor design is presented from the perspective of

wavelet tight frames. DoG is naturally a

wavelet, and the structure of the grid points array in our

descriptor is closely related to the spatial sampling of

wavelets. The DoG wavelet itself forms a frame, and when we modulate the parameters of our descriptor to

make the frame tighter, the performance of the DERF

descriptor improves accordingly. This is verified by

designing a tight frame DoG, which leads to much better

performance. Extensive experiments conducted in the image matching task on the multiview stereo

correspondence data set demonstrate that DERF

outperforms state of the art methods for both hand-crafted

and learned descriptors, while remaining robust and being

much faster to compute.

2015

18. MATLAB2015_18 Multi-task Pose-Invariant

Face Recognition

Face images captured in unconstrained environments

usually contain significant pose variation, which dramatically degrades the performance of algorithms

designed to recognize frontal faces. This paper proposes a

novel face identification framework capable of handling

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the full range of pose variations within ±90° of yaw. The

proposed framework first transforms the original pose-

invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face

representation scheme is then developed to represent the

synthesized partial frontal faces. For each patch,

a transformation dictionary is learnt under the proposed

multitask learning scheme. The transformation dictionary transforms the features of different poses into a

discriminative subspace. Finally, face matching is

performed at patch level rather than at the holistic level.

Extensive and systematic experimentation on FERET,

CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task-

based baselines as well as state-of-the-art methods for the

pose problem. We further extend the proposed algorithm

for the unconstrained face verification problem and

achieve top-level performance on the challenging LFW data set.

19. MATLAB2015_19 A Feature-Enriched

Completely Blind Image Quality Evaluator

Existing blind image quality assessment (BIQA)

methods are mostly opinion-aware. They learn regression models from training images with associated human

subjective scores to predict the perceptual quality of test

images. Such opinion-aware methods, however, require a

large amount of training samples with associated human

subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware methods often

have weak generalization capability, hereby limiting their

usability in practice. By comparison, opinion-unaware

methods do not need human subjective scores for training, and thus have greater potential for good

generalization capability. Unfortunately, thus far no

opinion-unaware BIQA method has shown consistently

better quality prediction accuracy than the opinion-aware

methods. Here, we aim to develop an opinion unaware BIQA method that can compete with, and perhaps

outperform, the existing opinion-aware methods. By

integrating the features of natural image statistics derived

from multiple cues, we learn a multivariate Gaussian

model of image patches from a collection of pristine natural images. Using the learned multivariate Gaussian

model, a Bhattacharyya-like distance is used to measure

the quality of each image patch, and then an overall

quality score is obtained by average pooling. The

proposed BIQA method does not need any distorted sample images nor subjective quality scores for training,

yet extensive experiments demonstrate its superior

quality-prediction performance to the state-of-the-art

opinion-aware BIQA methods.

2015

20. MATLAB2015_20 Spatiotemporal Saliency

Detection for Video

Sequences Based on

Random Walk With Restart

A novel saliency detection algorithm for video

sequences based on the random walk with restart (RWR)

is proposed in this paper. We adopt RWR to detect

spatially and temporally salient regions. More specifically, we first find a temporal saliency distribution

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using the features of motion distinctiveness, temporal

consistency, and abrupt change. Among them, the motion

distinctiveness is derived by comparing the motion profiles of image patches. Then, we employ the temporal

saliency distribution as a restarting distribution of the

random walker. In addition, we design the transition

probability matrix for the walker using the spatial features

of intensity, color, and compactness. Finally, we estimate the spatiotemporal saliency distribution by finding the

steady-state distribution of the walker.

The proposed algorithm detects foreground salient objects

faithfully, while suppressing cluttered backgrounds

effectively, by incorporating the spatial transition matrix and the temporal restarting distribution systematically.

Experimental results on various video sequences

demonstrate that the proposed algorithm outperforms

conventional saliency detection algorithms qualitatively

and quantitatively.

21. MATLAB2015_21 Sorted Consecutive Local

Binary Pattern

for Texture Classification

In this paper, we propose a sorted consecutive local

binary pattern (scLBP) for texture classification.

Conventional methods encode only patterns whose spatial transitions are not more than two, whereas scLBP

encodes patterns regardless of their spatial transition.

Conventional methods do not encode

patterns on account of rotation-invariant encoding; on the

other hand, patterns with more than two spatial transitions have discriminative power. The proposed scLBP encodes

all patterns with any number of spatial transitions while

maintaining their rotation-invariant nature by sorting the

consecutive patterns. In addition, we introduce dictionary learning of scLBP based on kd-tree which separates data

with a space partitioning strategy. Since the elements of

sorted consecutive patterns lie in different space, it can be

generated to a discriminative code with kd-tree. Finally,

we present a framework in which scLBPs and the kd-tree can be combined and utilized. The results

of experimental evaluation on five texture data sets—

Outex, CUReT, UIUC, UMD, and KTH-TIPS2-a—

indicate that our proposed framework achieves the best

classification rate on the CUReT, UMD, and KTH-TIPS2-a data sets compared with conventional methods.

The results additionally indicate that only a marginal

difference exists between the best classification rate

of conventional methods and that of the proposed

framework on the UIUC and Outex data sets.

2015

22. MATLAB2015_22 Robust 2D Principal

Component Analysis:

A Structured Sparsity Regularized Approach

Principal component analysis (PCA) is widely

used to extract features and reduce dimensionality in

various computer vision and image/video processing tasks. Conventional approaches either lack robustness to

outliers and corrupted data or are designed for one-

dimensional signals. To address this problem, we propose

a robust PCA model for two-dimensional

images incorporating structured sparse priors, referred to as structured sparse 2D-PCA. This robust model

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considers the prior of structured and grouped pixel values

in two dimensions. As the proposed formulation is jointly

nonconvex and nonsmooth, which is difficult to tackle by joint optimization, we develop a two-stage alternating

minimization approach to solve the problem. This

approach iteratively learns the projection matrices by

bidirectional decomposition and utilizes the proximal

method to obtain the structured sparse outliers. By considering the structured sparsity prior, the prop osed

model becomes less sensitive to noisy data and outliers in

two dimensions. Moreover, the computational cost

indicates that the robust two-dimensional model is

capable of processing quarter common intermediate format video in real time, as well as

handling large-size images and videos, which is often

intractable with other robust PCA approaches that involve

image-to-vector conversion. Experimental results on

robust face reconstruction, video background subtraction data set, and real-world videos

show the effectiveness of the proposed model compared

with conventional 2D-PCA and other robust PCA

algorithms.

23. MATLAB2015_23 Accurate Vessel

Segmentation With

Constrained B-Snake

We describean active contour framework with

accurate shape and size constraints on the vessel cross-

sectional planes to produce the vessel segmentation. It

starts with a multiscale vessel axis tracing in a 3D computed tomography (CT) data, followed by vessel

boundary delineation on the cross-sectional planes

derived from the extracted axis. The vessel boundary

surface is deformed under constrained movements on the cross sections and is voxelized to produce the final

vascular segmentation. The novelty of this paper lies

in the accurate contour point detection of thin vessels

based on the CT scanning model, in the efficient

implementation of missing contour points in the problematic regions and in the active contour model with

accurate shape and size constraints. The main advantage

of our framework is that it avoids disconnected and

incomplete segmentation of the vessels in the problematic

regions that contain touching vessels (vessels in close proximity to each other), diseased portions (pathologic

structure attached to a vessel), and thin vessels. It is

particularly suitable for accurate segmentation of thin and

low contrast vessels. Our method is evaluated and

demonstrated on CT data sets from our partner site, and its results are compared with three related

methods. Our method is also tested on two publicly

available databases and its results are compared with the

recently published method. The applicability of the

proposed method to some challenging clinical problems, the segmentation of the vessels in the problematic

regions, is demonstrated with good results on both

quantitative and qualitative experimentations; our

segmentation algorithm can delineate vessel boundaries

that have level of variability similar to those obtained

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manually.

24. MATLAB2015_24 PatchMatch With Potts

Model for Object

Segmentation and Stereo

Matching

This paper presents a unified variational formulation for

joint object segmentation and stereo matching, which

takes both accuracy and efficiency into account. In our

approach, depth-map consists of compact objects, each

object is represented through three different aspects: 1) the perimeter in image space; 2) the slanted object depth

plane; and 3) the planar bias, which is to add an additional

level of detail on top of each object plane in order to

model depth variations within an object. Compared with

traditional high quality solving methods in low level, we use a convex formulation of the multilabel Potts Model

with PatchMatch stereo techniques to generate depth-map

at each image in object level and show that accurate

multiple view reconstruction can be achieved with our

formulation by means of induced homography without discretization or staircasing artifacts. Our model is

formulated as an energy minimization that is optimized

via a fast primal-dual algorithm, which can handle several

hundred object depth segments efficiently. Performance

evaluations in the Middlebury benchmark data sets show that our method outperforms the traditional integer-

valued disparity strategy as well as the original

PatchMatch algorithm and its variants in subpixel

accurate disparity estimation. The proposed algorithm is

also evaluated and shown to produce consistently good results for various real-world data sets (KITTI benchmark

data sets and multiview benchmark

data sets).

2015

25. MATLAB2015_25 Robust Representation and

Recognition of

Facial Emotions Using

Extreme Sparse Learning

Recognition of natural emotions from human faces is an

interesting topic with a wide range of potential

applications like human-computer interaction, automated

tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally,

facial emotion recognition systems have been evaluated

on laboratory controlled data, which is not representative

of the environment faced in real-world applications. To

robustly recognize facial emotions in real-world natural situations, this paper proposes an approach called

Extreme Sparse Learning (ESL), which has the ability to

jointly learn a dictionary (set of basis) and a non-linear

classification model. The proposed approach combines

the discriminative power of Extreme Learning Machine (ELM) with the reconstruction property of sparse

representation to enable accurate classification when

presented with noisy signals and imperfect data

recorded in natural settings. Additionally, this work

presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework

is able to achieve state-of-the-art recognition accuracy on

both acted and spontaneous facial emotion databases.

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26. MATLAB2015_26 Adaptive Image Denoising

by Targeted Databases

We propose a data-dependent denoising procedure

to restore noisy images. Different from existing denoising

algorithms which search for patches from either the noisy image or a generic database, the new algorithm finds

patches from a database that contains relevant patches.

We formulate the denoising problem as an optimal filter

design problem and make two contributions. First, we

determine the basis function ofthe denoising filter by solving a group sparsity minimization problem. The

optimization formulation generalizes existing denoising

algorithms and offers systematic analysis of the

performance. Improvement methods are proposed to

enhance the patch search process. Second, we determine the spectral coefficients of the denoising filter by

considering a localized Bayesian prior. The

localized prior leverages the similarity of the targeted

database, alleviates the intensive Bayesian computation,

and links the new method to the classical linear minimum mean squared error estimation. We demonstrate

applications of the proposed method in a variety of

scenarios, including text images, multiview images,

and face images. Experimental results show the

superiority of the new algorithm over existing methods.

2015

27. MATLAB2015_27 Progressive Halftone

Watermarking Using

Multi-layer Table Lookup Strategy

In this work, a halftoning-based multi-layer watermarking

of low computational complexity is proposed. An

additional data hiding technique is also employed to embed multiple watermarks into the watermark to be

embedded to improve the security and embedding

capacity. At the encoder, the Efficient Direct Binary

Search (EDBS) method is employed to generate 256 reference tables to ensure the output is in halftone format.

Subsequently, watermarks are embedded by a set of

optimized compressed tables with various textural angles

for table lookup. At the decoder, the Least-MeanSquare

(LMS) metric is considered to increases the differences among those generated phenotypes.

2015

28. MATLAB2015_28 Learning Multiple Linear

Mappings for Efficient Single Image Super-

Resolution

Example learning-based superresolution (SR)

algorithms show promise for restoring a high-resolution (HR) image from a single low-resolution (LR) input. The

most popular approaches, however, are either time- or

space-intensive, which limits their practical applications

in many resource-limited settings. In this paper, we

propose a novel computationally efficient single image SR method that learns multiple linear

mappings (MLM) to directly transform LR feature

subspaces into HR subspaces. In particular, we first

partition the large nonlinear feature space of LR images

into a cluster of linear subspaces. Multiple LR subdictionaries are then learned, followed by inferring the

corresponding HR subdictionaries based on the

assumption that the LR–HR features share the same

representation coefficients. We establish MLM from the

input LR features to the desired HR outputs in order to achieve fast yet stable SR recovery. Furthermore, in order

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to suppress displeasing artifacts generated by the MLM -

based method, we apply a fast nonlocal means algorithm

to construct a simple yet effective similaritybased regularization term for SR enhancement. Experimental

results indicate that our approach is both quantitatively

and qualitatively superior to other application-oriented SR

methods, while maintaining relatively low time and space

complexity.

29. MATLAB2015_29 Cross-Domain Person Re-

Identification Using

Domain Adaptation Ranking SVMs

This paper addresses a new person re-identification

problem without label information of persons under non

overlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source

domain cameras, as well as unmatched (negative) and

unlabeled image pairs from target domain cameras, we

propose an Adaptive Ranking Support Vector Machines

(AdaRSVM) method for re-identification under target domain cameras without person labels. To overcome

the problems introduced due to the absence of matched

(positive) image pairs in the target domain, we relax the

discriminative constraint to a necessary condition only

relying on the positive mean in the target domain. To estimate the target positive mean, we make use of all the

available data from source and target domains as well as

constraints in person re-identification. Inspired by

adaptive learning methods, a new discriminative

model with high confidence in target positive mean and low confidence in target negative image pairs is

developed by refining the distance model learnt from the

source domain. Experimental results show that the

proposed AdaRSVM outperforms existing supervised or unsupervised, learning or non-learning reidentification

methods without using label information in target

cameras. Moreover, our method achieves better re-

identification performance than existing domain

adaptation methods derived under equal conditional probability assumption.

2015

30. MATLAB2015_30 Structure-Sensitive

Saliency Detection via Multilevel Rank

Analysis in

Intrinsic Feature Space

This paper advocates a novel multiscale,

structure-sensitive saliency detection method, which can distinguish multilevel, reliable saliency from various

natural pictures in a robust and versatile way. One key

challenge for saliency detection is to guarantee the entire

salient object being characterized differently from

nonsalient background. To tackle this, our strategy is to design a structure-aware descriptor based on the intrinsic

biharmonic distance metric. One benefit of introducing

this descriptor is its ability to simultaneously integrate

local and global structure information, which is extremely

valuable for separating the salient object from nonsalient background in a multiscale sense. Upon devising such

powerful shape descriptor, the remaining challenge is

to capture the saliency to make sure that salient subparts

actually stand out among all possible candidates. Toward

this goal, we conduct multilevel low-rank and sparse analysis in the intrinsic feature space spanned by the

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shape descriptors defined on over-segmented super-

pixels. Since the low-rank property emphasizes much

more on stronger similarities among super-pixels, we naturally obtain a scale space along the rank

dimension in this way. Multiscale saliency can be

obtained by simply computing differences among the

low-rank components across the rank scale. We conduct

extensive experiments on some public benchmarks, and make comprehensive, quantitative evaluation between our

method and existing state-of-the-art techniques. All the

results demonstrate the superiority of our method in

accuracy, reliability, robustness, and versatility.

31. MATLAB2015_31 Depth Reconstruction From

Sparse Samples:

Representation, Algorithm,

and Sampling

The rapid development of 3D technology and

computer vision applications has motivated a thrust of

methodologies for depth acquisition and estimation.

However, existing hardware and software acquisition methods have limited performance due to poor depth

precision, low resolution, and high computational cost. In

this paper, we present a computationally efficient method

to estimate dense depth maps from sparse measurements.

There are three main contributions. First, we provide empirical evidence that depth maps can be encoded much

more sparsely than natural images using common

dictionaries, such as wavelets and contourlets. We also

show that a combined wavelet–contourlet dictionary

achieves better performance than using either dictionary alone. Second, we propose an alternating direction

method of multipliers (ADMM) for depth map

reconstruction. A multiscale warm start procedure

is proposed to speed up the convergence. Third, we propose a two-stage randomized sampling scheme to

optimally choose the sampling locations, thus maximizing

the reconstruction performance for a given sampling

budget. Experimental results show that the proposed

method produces high-quality dense depth estimates, and is robust to noisy measurements. Applications to real data

in stereo matching are demonstrated.

2015

32. MATLAB2015_32 Image Denoising by Exploring External

and Internal Correlations

Single image denoising suffers from limited data collection within a noisy image. In this paper, we propose

a novel image denoising scheme, which explores both

internal and external correlations with the help of web

images. For each noisy patch, we build internal and

external data cubes by finding similar patches from the noisy and web images, respectively. We then propose

reducing noise by a two-stage strategy using different

filtering approaches. In the first stage, since the noisy

patch may lead to inaccurate patch selection, we

propose a graph based optimization method to improve patch matching accuracy in external denoising. The

internal denoising is frequency truncation on internal

cubes. By combining the internal and external denoising

patches, we obtain a preliminary denoising result. In the

second stage, we propose reducing noise by filtering of external and internal cubes, respectively, on transform

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domain. In this stage, the preliminary denoising result

not only enhances the patch matching accuracy but also

provides reliable estimates of filtering parameters. The final denoising image is obtained by fusing the external

and internal filtering results. Experimental results show

that our method constantly outperforms state-of-the-art

denoising schemes in both subjective and objective

quality measurements, e.g., it achieves >2 dB gain compared with BM3D at a wide range of noise levels.

33. MATLAB2015_33 Motion-Compensated

Coding and Frame Rate Up-Conversion: Models

and Analysis

Block-based motion estimation (ME) and motion

compensation (MC) techniques are widely used in modern video processing algorithms and compression

systems. The great variety of video applications and

devices results in diverse compression specifications,

such as frame rates and bit rates.

In this paper, we study the effect of frame rate and compression bit rate on block-based ME and MC as

commonly utilized in inter-frame coding and frame rate

up-conversion (FRUC). This joint examination yields a

theoretical foundation for comparing MC procedures in

coding and FRUC. First, the video signal is locally modeled as a noisy translational motion of an image.

Then, we theoretically model the motion-compensated

prediction of available and absent frames as in coding and

FRUC applications, respectively. The theoretic MC-

prediction error is studied further and its autocorrelation function is calculated, yielding useful separable-

simplifications for the coding application. We argue that a

linear relation exists between the variance of the MC-

prediction error and temporal distance. While the relevant distance in MC coding is between the predicted and

reference frames, MC-FRUC is affected by the distance

between the frames available for interpolation. We

compare our estimates with experimental results and

show that the theory explains qualitatively the empirical behavior. Then, we use the models proposed to analyze a

system for improving of video coding at low bit rates,

using a spatiotemporal scaling. Although this concept is

practically employed in various forms, so far it lacked a

theoretical justification. We here harness the proposed MC models and present a comprehensive analysis of the

system, to qualitatively predict the experimental

results.

2015

34. MATLAB2015_34 Fractal Analysis for

Reduced Reference

Image Quality Assessment

In this paper, multifractal analysis is adapted to

reduced-reference image quality assessment (RR-IQA). A

novel RR-QA approach is proposed, which measures the

difference of spatial arrangement between the reference

image and the distorted image in terms of spatial regularity measured by fractal dimension. An image is

first expressed in Log-Gabor domain. Then, fractal

dimensions are computed on each Log-Gabor subband

and concatenated as a feature vector. Finally, the

extracted features are pooled as the quality score of the distorted image using 1 distance. Compared with existing

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approaches, the proposed method measures image quality

from the perspective of the spatial distribution of image

patterns. The proposed method was evaluated on seven public benchmark data sets. Experimental results have

demonstrated the excellent performance of the proposed

method in comparison with state-of-the-art approaches.

35. MATLAB2015_35 Criteria-Based Modulation

for Multilevel Inverters

Pulse-width modulation schemes are aimed at adjusting

the fundamental component while reducing the harmonic

content of an inverter output voltage or current. This

paper addresses the topic of optimal inverter operation in

reference to a given objective function. The objective function could embody either a single performance

criterion, such as voltage or current total harmonic

distortion, or a weighted sum of multiple criteria.

The proposed method ensures primacy of the chosen

solution while imposing no restriction over its modulation index. In particular, operating the inverter by the chosen

solution would result in performances superior to any

other modulation scheme commutating in an equal

number of switching angles per fundamental cycle. The

proposed method allows for the consideration of practical inverter constraints and prevents the

possibility of impractical switching sequence. A detailed

investigation of the method is given, accompanied by two

practical cases minimizing, respectively, phase-voltage

THD and line-current THD of a three level inverter. Selected simulation and experimental results are

presented to validate the theoretical part.

2015

36. MATLAB2015_36 A Fully Soft-Switched

Single Switch Isolated

DC-DC Converter

This paper proposes a soft-switched single switch

isolated converter. The proposed converter is able to offer

low cost and high power density in step up application

due to the following features: ZCS turn-on and ZVS turn-

off of switch and ZCS turn-off of diodes regardless of voltage and load variation; low rated lossless snubber;

reduced transformer volume compared to flyback based

converters due to low magnetizing current. Experimental

results on a 100kHz, 250W prototype are provided to

validate the proposed concept.

2015

37. MATLAB2015_37 Functional Modeling of

Symmetrical Multipulse Autotransformer Rectifier

Units

for Aerospace Applications

This paper aims to develop a functional model of

symmetrical multipulse autotransformer rectifier units (ATRUs) for more-electric aircraft (MEA) applications.

The ATRU is seen as the most reliable way readily to be

applied in the MEA. Interestingly, there is no model of

ATRUs suitable for unbalanced or faulty

conditions at the moment. This paper is aimed to fill this gap and develop functional models suitable for both

balanced and unbalanced conditions. Using the fact that

the dc voltage and current are strongly related to the

voltage and current vectors at the ac terminals of ATRUs,

a functional model has been developed for the asymmetric ATRUs. The developed functional models

are validated through simulation and experiment. The

efficiency of the developed model is also demonstrated by

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comparing with corresponding detailed switching models.

The developed functional model shows significant

improvement of simulation efficiency, especially under balanced conditions.

38. MATLAB2015_38 Model Predictive Control

Methods to Reduce Common-Mode Voltage

for Three-Phase Voltage

Source Inverters

In this paper, we propose model predictive control

methods to reduce the common-mode voltage of three-phase voltage source inverters (VSIs). In the reduced

common-mode voltage-model predictive control (RCMV-

MPC) methods proposed in this paper, only nonzero

voltage vectors are utilized to reduce the common-mode

voltage as well as to control the load currents. In addition, two nonzero voltage vectors are selected from the cost

function at every sampling period, instead of using only

one optimal vector during one sampling period. The two

selected nonzero vectors are distributed in one sampling

period in such a way as to minimize the error between the measured load current and the reference. Without

utilizing the zero vectors, the common-mode voltage

controlled by the proposed RCMV-MPC algorithms can

be restricted within ±Vdc/6. Furthermore, application of

the two nonzero vectors with optimal time sharing between them can yield satisfactory load current ripple

performance without using the zero vectors. Thus, the

proposed RCMV-MPC methods can reduce the common-

mode voltage as well as control the load currents with fast

transient response and satisfactory load current ripple performance compared with the conventional model

predictive control method. Simulation and experimental

results are included to verify the effectiveness of the

proposed RCMV-MPC methods.

2015

39. MATLAB2015_39 Interleaved Phase-Shift

Full-Bridge Converter With

Transformer Winding Series–Parallel

Autoregulated

(SPAR) Current Doubler

Rectifier

The analysis and design guidelines for a two-phase

interleaved phase-shift full-bridge converter with

transformer winding series–parallel autoregulated current doubler rectifier are presented in this paper. The

secondary windings of two transformers

work in parallel when the equivalent duty cycle is smaller

than 0.25 but in series when the duty cycle is larger than

0.25 owing to the series–parallel autoregulated rectifier. With the proposed rectifying structure, the voltage stress

of the rectifier is reduced. Also, the interleaving operation

reduces the output current ripple. A 1-kW prototype with

200–400-V input and 50-V/20-A output is built up

to verify the theoretical analysis.

2015

40. MATLAB2015_40 Analysis of Active-

Network Converter with Coupled

Inductors

High step-up voltage gain DC/DC converters are widely

applied in fuel cell stacks, photovoltaic arrays, battery sources, and high intensity discharge (HID) lamps

power systems. Active-network converters with coupled

inductors (CL-ANC) are derived from switched inductor

active-network converters (SL-ANC). The proposed

converter contains two coupled inductors which can be integrated into one magnetic core and two power

switches. The converter can provide a relatively high

voltage conversion ratio with a small duty cycle; the

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voltage and current stress of power switches are low

which is helpful to reduce the losses. This paper shows

the key waveforms of the CL-ANC and detailed derivation of the steady-state operation principle. The

voltage conversion ratio and the effect of the leakage

inductance on voltage gain are discussed. The voltage

stress and current stress on the power devices are

illustrated and the comparison between the proposed converter and SL-ANC are given. Finally, the prototype

has been established in the lab with 200V and 400V

output under different turn ratios. Experimental results are

given to verify the correctness of the analysis.

41. MATLAB2015_41 Modeling and Controller

Design of a Semi-Isolated

Multi-Input Converter for

Hybrid PV/Wind Power Charger System

The objective of this paper is to propose the

development of a multi-input dc-dc converter (MIC)

family which is composed of isolated and/or non-isolated

dc-dc converters. By analyzing five basic isolated dc-dc converters, four isolated pulsating voltage source cells (I-

PVSCs) and three isolated pulsating current source cells

(I-PCSCs) are generated. Moreover, a semi-isolated

multi-input converter (S-MIC) for hybrid PV/wind power

charger system which can simplify the power system, reduce the cost, deliver continuous power and

overcome high voltage-transfer-ratio problems is

proposed. In this paper, the operational principle of the

proposed S-MIC is explained, the small-signal ac model

is derived and the controller design is developed. Computer simulations and experimental results are

presented to verify the accuracy of the proposed small

signal ac model and the performance of the proposed S-

MIC.

2015

42. MATLAB2015_42 A Four-Switch Three-

Phase SEPIC-Based

Inverter

The four-switch three-phase (FSTP) inverter has been

proposed as an innovative inverter design to

reduce the cost, complexity, size, and switching losses of the DC-AC conversion system. Traditional FSTP inverter

usually operates at half the DC input voltage, hence, the

output line voltage cannot exceed this value. This paper

proposes a novel design for the FSTP inverter based on

the topology of the single-ended primary-inductance converter (SEPIC). The proposed topology provides pure

sinusoidal output voltages with no need for output filter.

Compared to traditional FSTP inverter, the proposed

FSTP SEPIC inverter improves the voltage utilization

factor of the input DC supply, where the proposed topology provides higher output line voltage which can be

extended up to the full value of the DC input voltage. The

integral sliding-mode control is used with the proposed

topology to optimize its dynamics and to ensure

robustness of the system during different operating conditions. Derivation of the equations describing the

parameters design, components ratings, and the operation

of the proposed SEPIC inverter is presented in this paper.

Simulation model and experimental setup are used to

validate the proposed concept. Simulations and experimental results show the effectiveness of the

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proposed inverter.

43. MATLAB2015_43 High-Efficiency Isolated Single-Input Multiple-

Output

Bidirectional Converter

This study presents a high-efficiency isolated single-input multiple-output bidirectional (HISMB) converter for a

power storage system. According to the power

management, the proposed HISMB converter can operate

at a step-up state (energy release) and a step-down state

(energy storage). At the step-up state, it can boost the voltage of a low-voltage input power source to a high-

voltage-side dc bus and middle-voltage terminals. When

the high-voltage-side dc bus has excess energy, one can

reversely transmit the energy. The high-voltage dc bus

can take as the main power, and middle-voltage output terminals can supply powers for individual middle-

voltage dc loads or to charge auxiliary power sources

(e.g., battery modules). In this study, a coupled-inductor-

based HISMB converter accomplishes the bidirectional

power control with the properties of voltage clamping and soft switching, and the corresponding device

specifications are adequately designed. As a result, the

energy of the leakage inductor of the coupled inductor

can be recycled and released to the high-voltage-side dc

bus and auxiliary power sources, and the voltage stresses on power switches can be greatly reduced. Moreover, the

switching losses can be significantly decreased because of

all power switches with zero-voltage-switching (ZVS)

features. Therefore, the objectives of high-efficiency

power conversion, electric isolation, bidirectional energy transmission, and various output voltage with different

levels can be obtained. The effectiveness of the proposed

HISMB converter is verified by experimental results of a

kW-level prototype in practical applications.

2015

44. MATLAB2015_44 Modularized Control

Strategy and Performance

Analysis of DFIG System under Unbalanced and

Harmonic Grid Voltage

The paper presents a modularized control

strategy of doubly fed induction generator (DFIG)

system, including the grid-side converter (GSC) and rotor-side converter (RSC), under unbalanced and

harmonic grid voltage. The sequence decomposition

process and complicated control reference calculation can

be avoided in the proposed control strategy. From the

perspective of power grid friendly-operation, the control targets of DFIG system in this paper are chosen as: 1)

smooth active and reactive power injected into the power

grid; 2) balanced and sinusoidal current injected into the

power grid. The RSC and GSC can work as two

independent modules and the communication between RSC and GSC can be removed. Furthermore, the 3rd

harmonic current component, DC link voltage fluctuation

and electromagnetic torque pulsation under the different

control targets are theoretically analyzed. Finally, the

availability of the proposed modularized control strategy of DFIG system under unbalanced and distorted grid

voltage is verified by experiment results.

2015

45. MATLAB2015_45 Resonant Switched-

Capacitor Voltage

Regulator with Ideal

A new, small and efficient voltage regulator,

realized using a resonant switched capacitor converter

technology, is introduced. Voltage regulation is

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Transient Response

implemented by means of simple digital pulse density

modulation. It displays an ideal transient response with a

zero-order nature to all disturbance types. The newly developed topology acts as a gyrator with a wide range of

voltage conversion ratios (below as well as above unity)

with constant efficiency characteristics for the entire

operation range. The operation of the voltage regulator is

verified on a 20W experimental prototype, demonstrating ideal transient recovery without over/under-shoots in

response to load and line transients. Simple design

guidelines for the voltage regulation system are provided

and verified by experiments.

46. MATLAB2015_46 On the Performance of

Multiobjective

Evolutionary

Algorithms in Automatic Parameter Extraction of

Power Diodes

In this paper, a general, robust, and automatic

parameter extraction of nonlinear compact models is

presented. The parameter extraction is based on

multiobjective optimization using evolutionary algorithms which allow fitting of several highly

nonlinear and highly conflicting characteristics

simultaneously. Two multiobjective evolutionary

algorithms which have been proved to be robust for a

wide range of multiobjective problems [1]–[3], the Nondominated Sorting Genetic Algorithm II

and the Multiobjective Covariance Matrix Adaptation

Evolution Strategy, are used in the parameter extraction

of a novel power diode compact model based on the

lumped charge technique. The performance of the algorithms is assessed using a systematic statistical

approach. Good agreement between the simulated and

measured characteristics of the power diode shows the

accuracy of the used compact model and the efficiency and effectiveness of the proposed multiobjective

optimization scheme.

2015

47. MATLAB2015_47 Development of a Wind

Interior Permanent-Magnet

Synchronous Generator

Based Microgrid and Its Operation Control

This paper presents the development of a wind

interior permanent-magnet synchronous generator

(IPMSG) based DC micro-grid and its operation control.

First, the derated characteristics of PMSG systems with various AC/DC converters and operation controls are

comparatively analyzed. Then the IPMSG followed by

three-phase Vienna switch mode rectifier (SMR) is

developed to establish the common DC bus of DC micro-

grid. Good developed power and voltage regulation characteristics are achieved via the proposed

commutation tuning, robust current and voltage controls.

Second, a single-phase three-wire (1P3W) inverter is

constructed to serve as the test load. Good AC

220V/110V output voltage waveforms under unknown and nonlinear loads are preserved by the developed robust

waveform tracking control scheme. Third, a battery

energy storage system (BESS) is established, and the fast

energy storage support response is obtained via the

proposed droop control approach with adaptive predictive current control method. In addition, a chopped dump load

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is equipped to enhance the energy balance control

flexibility.

48. MATLAB2015_48 A Novel Drive Method for

High-Speed

Brushless DC Motor

Operating in a Wide Range

In this paper, a novel drive method, which is different

from the traditional motor drive techniques, for high-

speed brushless DC (BLDC) motor is proposed and

verified by a series of experiments. It is well known that the BLDC motor can be driven by either Pulse-Width

Modulation (PWM) techniques with a constant DC-link

voltage or Pulse-Amplitude Modulation (PAM)

techniques with an adjustable DC-link voltage. However,

to our best knowledge, there is rare study providing a proper drive method for high-speed BLDC motor with a

large power over a wide speed range. Therefore, the

detailed theoretical analysis comparison of the PWM

control and the PAM control for high-speed BLDC motor

is first given. Then a conclusion that the PAM control is superior to the PWM control at high speed is obtained

because of decreasing the commutation delay and high

frequency harmonic wave. Meanwhile, a new high-speed

BLDC motor drive method based on the hybrid approach

combining PWM and PAM is proposed. At last, the feasibility and effectiveness of the performance analysis

comparison and the new drive method are verified by

several experiments.

2015

49. MATLAB2015_49 The Dynamic Control of

Reactive Power for the

Brushless Doubly Fed

Induction Machine with Indirect Stator-quantities

Control Scheme

Compared to the doubly fed induction

machine (DFIM), the brushless doubly fed induction

machine (BDFIM) has higher reliability by virtue of the

absence of a brush gear. Recent research on structure optimization design and control strategy of BDFIM has

made remarkable progress. BDFIM indirect

stator-quantities control (ISC) is a new control strategy,

which, in comparison to vector control strategy, requires

fewer parameters and does not need rotating coordinate transformation. This paper further develops the dynamic

control of reactive power for the BDFIM with ISC

scheme. Detailed theoretical analysis is done to show the

controller structure of the reactive power. The

experimental results of the prototype show the feasibility of the proposed scheme. As a result, the proposed ISC

controllers have been able to control not only speed and

torque, but also the reactive power.

2015

50. MATLAB2015_50 An LCL-LC Filter for

Grid-Connected

Converter: Topology,

Parameter and Analysis

In order to further cut down the cost of filter for

grid-connected pulse width modulation (PWM) converter

under the more and more stringent grid code, a new kind

of high order filter, named LCL-LC filter, is presented in this paper. The resonant frequency characteristics of the

filter are analyzed and a parameter design method on the

base of the characteristics is also proposed in the paper.

The proposed parameter design method can easily make

full use of the existing research results about the traditional LCL filter parameter design. And then a

parameter robustness analysis method based on

four-dimensional graphics is proposed to analyze

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parameter robustness of the presented filter. Compared

with the traditional one, the proposed analysis method can

analyze the filter performance under variations of several parameters at a time without any iteration. The

comparative analysis and discussion considering the LCL

filter, the trap filter, and the LCL-LC filter,

are presented and verified through the experiments on a

5kW grid-connected converter prototype. Experiment results demonstrate the accuracy of theoretical analysis

and prove the presented filter has a better performance

than two others.

51. MATLAB2015_51 3D microtransformers for

DC-DC on-chip

power conversion

We address the miniaturization of power converters by

introducing novel, 3D micro transformers with magnetic

core for low-MHz frequency applications. The core is

fabricated by lamination and micro structuring of

Metglas® 2714A magnetic alloy. The solenoids of the micro transformers are wound around the core using a

ball-wedge wire bonder. The wire bonding process is fast,

allowing the fabrication of solenoids with up to 40 turns

in 10 s. The fabricated devices yield the high inductance

per unit volume of 2.95 µH/mm3 and energy per unit volume of 133 nJ/mm3 at the frequency of 1 MHz. The

power efficiency of 64-76% are measured for different

turns ratio with coupling factors as high as 98%.

2015

52. MATLAB2015_52 Indirect Matrix Converter-

Based Topology

and Modulation Schemes

for Enhancing Input Reactive Power

Capability

A new topology based on indirect matrix converter

(IMC) is proposed to enhance the input reactive power

capability. This topology consists of a conventional IMC

and an auxiliary switching network (ASN), which is connected to the dc-link of the IMC in parallel. With the

aid of ASN, an implicit current source converter-based

static synchronous compensator can be embedded

into an IMC, which lays a foundation for the input

reactive power control. Based on the proposed topology, two modulation schemes are presented, and the

formations of the output voltage and input reactive

current are decoupled in both of them. To minimize

power loss and improve input current quality, a double

closed-loop control algorithm is introduced, in which the current through the dc inductor in ASN is controlled to be

minimum. Different from the conventional IMC, the input

reactive power of the topology is independent of its load

condition without considering the practical constraints.

The effectiveness of the proposed topology and modulation scheme is confirmed by experimental results.

2015

53. MATLAB2015_53 Closed Loop Discontinuous

Modulation Technique for Capacitor Voltage

Ripples and Switching

Losses

Reduction in Modular

Multilevel Converters

In this paper, a new discontinuous modulation

technique is presented for the operation of the modular multilevel converter (MMC). The modulation technique

is based on adding a zero-sequence to the original

modulation signals so that the MMC arms are clamped to

the upper or lower terminals of the dc-link bus. The

clamping intervals are controlled according to the absolute value of the output current to minimize the

switching losses of the MMC. A significant reduction in

the capacitor voltage ripples is achieved, especially when

2015

NEXGEN TECHNOLOGY




operating with low modulation indices. Furthermore, a

circulating current control strategy suitable for this

modulation technique is also proposed. Simulation and experimental results under various operating points are

reported along with evaluation and comparison results

against a conventional carrier-based pulse-width

modulation method.

54. MATLAB2015_54 Decentralized Inverse-

Droop Control for

Input-Series-Output-

Parallel DC-DC Converters

Input-series-output-parallel (ISOP) DC-DC converters are

suited for high input-voltage and low output-voltage

applications. This letter presents a decentralized inverse-

droop control for this configuration. Each module is self-contained and no central controller is needed, thus

improving the system modularity, reliability and

flexibility. With the proposed inverse-droop control,

the output voltage reference rises as the load becomes

heavy. Even though the input voltages is not used in the inverse-droop loop, the power sharing including input

voltage sharing (IVS) and output current sharing (OCS)

can still be well achieved. Besides, the output voltage

regulation characteristic is not affected by the variation

of input voltage. The operation principle is introduced, and stability of the strategy is also revealed based on

small signal modeling. Finally, the experiment is

conducted to verify the effectiveness of the control

strategy.

2015

55. MATLAB2015_55 Detailed Analysis of DC-

Link Virtual Impedance

based Suppression Method

for Harmonics Interaction in High-Power PWM

Current-Source Motor

Drives

For high-power PWM current-source motor drive

systems, due to the low converter switching frequency

and the relative small dc choke for reduced cost/weight,

the converters’ switching harmonics may interact through dc link and produce inter harmonics in the entire system.

Such harmonics interaction phenomenon may give rise to

the system resonance at certain motor speeds, which

degrades the grid-side power quality and generates

excessive torque ripples on the motor side. The resonance caused by the harmonics interaction in high-power PWM

current-source motor drives is investigated in previous

work. In addition, to actively suppress such resonance,

the basic idea of a dc-link virtual impedance based

suppression method has also been proposed. This paper extends the previous work to thoroughly analyze the

mechanism and realization of resonance suppression by

the dc-link virtual impedance based method. The indepth

analysis shows that the dc-link virtual impedance based

method successfully enables the active inter harmonics compensation capability of high-power PWM current-

source drives, which is not addressed in previous

researches. Moreover, simulations and experiments

demonstrate that, by following the selection of coefficient

in the suppression method discussed in this paper, the dc-link virtual impedance based method can effectively

enhance the attenuation effect of dc link in high-power

PWM current source drive systems so as to suppress the

resonance due to the harmonics interaction under all

resonance conditions.

2015

NEXGEN TECHNOLOGY




56. MATLAB2015_56 An Online Frequency-

Domain Junction

Temperature Estimation Method for IGBT Modules

This letter proposes a new frequency-domain

thermal model for online junction temperature estimation

of insulated-gate bipolar transistor (IGBT) modules. The proposed model characterizes the thermal behavior of an

IGBT module by a linear time-invariant (LTI) system,

whose frequency response is obtained by applying the fast

Fourier transform (FFT) to the time derivative of the

transient thermal impedance from junction to a reference position of the IGBT module. The junction

temperature of the IGBT is then estimated using the

frequency responses of the LTI system and the heat

sources of the IGBT module. Simulation results show that

the proposed method is computationally efficient for an accurate online junction temperature estimation of

IGBT modules in both steady-state and transient loading

conditions.

2015

57. MATLAB2015_57 Characterization of a

Silicon IGBT and Silicon

Carbide MOSFET Cross

Switch Hybrid

A parallel arrangement of a Silicon (Si) IGBT and a

Silicon Carbide (SiC) MOSFET is experimentally

demonstrated. The concept referred to as the Cross

Switch “XS” hybrid aims to reach optimum power device

performance by providing low static and dynamic losses while improving the overall electrical and thermal

properties due to the combination of both the bipolar Si

IGBT and unipolar SiC MOSFET characteristics. For the

purpose of demonstrating the XS hybrid, the parallel

configuration was implemented experimentally in a single package for devices rated at 1200V. Test results were

obtained to validate this approach with respect to the

static and dynamic performance when compared to

a full Si IGBT and a full SiC MOSFET reference devices having the same power ratings as for the XS hybrid

samples.

2015

58. MATLAB2015_58 LCL Filter Design and Inductor Current Ripple

Analysis for 3-

level NPC Grid Interface

Converter

The harmonic filter for a 3-level neutral point clamped (NPC) grid interface converter is designed in this

paper with good filtering performance and small

component size. LCL topology is selected because of the

attenuation and size tradeoff. The design of the inverter

side inductor L1 is emphasized due to its cost. A detailed inductor current ripple analysis is given based on the

space vector modulation (SVM). The analysis derives the

inductor volt-second and the maximum current

ripple equation in line cycle. It also reveals the switching

cycle current ripple distribution over a line cycle, with the consideration of power factor. The total system loss is

calculated with different ripple current. Inductor L1 is

determined by the loss and size tradeoff. Also the

capacitor and grid side inductor L2 is designed based on

attenuation requirement. Different damping circuits for LCL filter are compared and investigated in detail. The

filter design is verified by both simulation and a 200kVA

3-level NPC converter hardware.

2015

59. MATLAB2015_59 Virtual RC Damping of Active damping and harmonic compensation are 2015

NEXGEN TECHNOLOGY




LCL-Filtered Voltage

Source Converters with

Extended Selective Harmonic Compensation

two common challenges faced by LCL-filtered voltage

source converters. To manage them holistically, this

paper begins by proposing a virtual RC damper in parallel with the passive filter capacitor. The virtual damper is

actively inserted by feeding back the passive capacitor

current through a high-pass filter, which indirectly,

furnishes two superior features. They are the

mitigation of phase lag experienced by a conventional damper and the avoidance of instability caused by the

negative resistance inserted unintentionally. Moreover,

with the virtual RC damper, the frequency region, within

which the harmonic compensation is effective, can be

extended beyond the gain crossover frequency. This is of interest to some high-performance applications, but has

presently not been achieved by existing schemes.

Performance of the proposed scheme has been tested in

the laboratory with results obtained for demonstrating

stability and harmonic compensation.

60. MATLAB2015_60 Versatile Control of

Unidirectional AC-DC

Boost Converters for Power Quality Mitigation

This paper introduces a versatile control scheme for

unidirectional ac-dc boost converters for the purpose of

mitigating grid power quality. Since most power factor correction circuits available in the commercial market

utilize unidirectional ac-dc boost converter topologies,

this is an almost no-cost solution for compensating

harmonic current and reactive power in residential

applications. Harmonic current and reactive power compensation methods in the unidirectional ac-dc boost

converter are investigated. The additional focus of this

paper is to quantify the input current distortions by the

unidirectional ac-dc boost converter used for supplying not only active power to the load but also reactive power.

Due to input current distortions, the amount of reactive

power injected from an individual converter to the grid

should be restricted. Experimental results are presented to

validate the effectiveness of the proposed control method.

2015

61. MATLAB2015_61 Aalborg Inverter — A new

type of “Buck in

Buck, Boost in Boost” Grid-tied Inverter

This paper presents a new family of high

efficiency DC/AC grid-tied inverter with a wide

variation of input DC voltage. It is a “Boost in Boost, Buck in Buck” inverter, meaning that only one power

stage works at high frequency in order to achieve

minimum switching loss. The minimum voltage drop of

the filtering inductor in the power loop is achieved to

reduce the conduction power loss in both “Boost” and “Buck” mode. The principle of operation is

demonstrated through the analysis on the equivalent

circuits of a “half-bridge” single-phase inverter. The

theoretical analysis shows that when input DC voltage

is larger than the magnitude of the AC voltage, it is a Voltage Source Inverter (VSI), and on the contrary it is

Current Source Inverter (CSI) in the other mode. A

220 V/50 Hz/ 2000 W prototype has been constructed.

Simulations and experiments show it has a good control

and system performance.

2015

NEXGEN TECHNOLOGY




62. MATLAB2015_62 Grid-connected Forward

Micro-inverter with

Primary-Parallel

Secondary-Series Transformer

This paper presents a primary-parallel secondaryseries

multicore forward micro-inverter for photovoltaic

ACmodule application. The presented micro-inverter

operates with a constant off-time boundary mode control, providing MPPT capability and unity power factor. The

proposed multi transformer solution allows using low-

profile unitary turns ratio transformers. Therefore, the

transformers are better coupled and the overall

performance of the micro-inverter is improved. Due to the multiphase solution the number of devices increases but,

the current stress and losses per device

are reduced contributing to an easier thermal

management. Furthermore, the decoupling capacitor is

split among the phases, contributing to a low-profile solution without electrolytic capacitors suitable to be

mounted in the frame of a PV module. The proposed

solution is compared to the classical parallel interleaved

approach, showing better efficiency in a wide power

range and improving the weighted efficiency.

2015

63. MATLAB2015_63 A Single-Stage

PhotoVoltaic System for a DualInverter fed Open-End

Winding Induction Motor

Drive for Pumping

Applications

This paper presents an integrated solution for

PhotoVoltaic (PV) fed water-pump drive system, which uses an Open-End Winding Induction Motor (OEWIM).

The dualinverter fed OEWIM drive achieves the

functionality of a threelevel inverter and requires low

value DC bus voltage. This helps in an optimal

arrangement of PV modules, which could avoid large strings and helps in improving the PV performance with

wide band-width of operating voltage. It also reduces the

voltage rating of the DC-link capacitors and switching

devices used in the system. The proposed control strategy

achieves an integration of both Maximum Power Point Tracking (MPPT) and V/f control for the efficient

utilization of the PV panels and the motor. The

proposed control scheme requires the sensing of PV

voltage and current only. Thus, the system requires less

number of sensors. All the analytical, simulation and experimental results of this work under different

environmental conditions are presented in this paper.

2015

NEXGEN TECHNOLOGY




MATLAB PROJECTS 2014

SN PRO JECT CO DE

PRO JECT TO PIC YEAR

1

MAT1425

Topic: Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images

Abstract: Diabetic retinopathy (DR) is a micro vascular complication of long-term diabetes and it is the major cause of visual impairment because of changes in blood vessels of the retina. Major vision loss because of DR is highly preventable with regular screening and timely intervention at the earlier stages. The

presence of exudates is one of the primitive signs of DR and the detection of these exudates is the first step in automated screening for DR. Hence, exudates detection becomes a significant diagnostic task, in which digital retinal imaging plays a vital role. In this study, the authors propose an algorithm to detect the

presence of exudates automatically and this helps the ophthalmologists in the diagnosis and follow-up of DR. Exudates are normally detected by their high grey-level variations and they have used an artificial neural network to perform this task by applying colour, size, shape and texture as the features. The performance of the authors algorithm has been prospectively tested by using DIARETDB1 database and

evaluated by comparing the results with the ground-truth images annotated by expert ophthalmologists. They have obtained illustrative results of mean sensitivity 96.3%, mean specificity 99.8%, using lesion-based evaluation criterion and achieved a classification accuracy of 99.7%.

2014

2

MAT1424

Topic: Data Hiding in Encrypted H.264/AVC Video Streams by Codeword Substitution Abstract: Digital video sometimes needs to be stored and processed in an encrypted format to maintain security and privacy. For the purpose of content notation and/or tampering detection, it is necessary to

perform data hiding in these encrypted videos. In this way, data hiding in encrypted domain without decryption preserves the confidentiality of the content In addition, it is more efficient without decryption followed by data hiding and re-encryption. In this paper, a novel scheme of data hiding directly in the encrypted version of H.264/AVC video stream is proposed, which includes the following three parts, i.e.,

H.264/AVC video encryption, data embedding, and data extraction. By analyzing the property of H.264/AVC codec, the code words of intra prediction modes, the code words of motion vector differences, and the code words of residual coefficients are encrypted with stream ciphers. Then, a data hider may embed

additional data in the encrypted domain by using codeword substitution technique, without knowing the original video content. In order to adapt to different application scenarios, data extraction can be done either in the encrypted domain or in the decrypted domain. Furthermore, video file size is strictly preserved even after encryption and data embedding. Experimental results have demonstrated the feasibility and efficiency

of the proposed scheme.

2014

3

MAT1423

Topic: Edge Detection Method for Image Processing based on Generalized Type -2 Fuzzy Logic Abstract: This paper presents an edge detection method based on the morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement generalized type-2 fuzzy logic for edge detection. For the defuzzification process, the heights and approximation methods are used. Simulation results with a type-1 fuzzy inference system (T1FIS), an

interval type-2 fuzzy inference system (IT2FIS) and with a generalized type-2 fuzzy inference system (GT2FIS) for edge detection are presented. The proposed generalized type-2 fuzzy edge detection method

2014

NEXGEN TECHNOLOGY




was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of using generalized type-2 fuzzy logic.

4

MAT1422

Topic: Deblurred images post-processing by Poisson warping Abstract: In this work we develop a post-processing algorithm which enhances the results of the existing

image deblurring methods. It performs additional edge sharpening using grid warping. The idea of the proposed algorithm is to transform the neighborhood of the edge so that the neighboring pixels move closer to the edge, and then resample the image from the warped grid to the original uniform grid. The proposed technique preserves image textures while making the edges sharper. The effectiveness of the method is

shown for basic deblurring methods on LIVE database images with added blur and noise.

2014

5

MAT1421

Topic: Image Contrast Enhancement Using Color and Depth Histograms Abstract: In this letter, we propose a new global contrast enhancement algorithm using the histograms of color and depth images. On the basis of the histogram-modification framework, the color and depth image

histograms arefirst partitioned into subintervals using the Gaussian mixture model. The positions partitioning the color histogram are then adjusted such that spatially neighboring pixels with the similar intensity and depth values can be grouped into the same sub-interval. By estimating the mapping curve of the contrast

enhancement for each sub-interval, the global image contrast can be improved without over-enhancing the local image contrast. Experimental results demonstrate the effectiveness of the proposed algorithm.

2014

6

MAT1420

Topic: O bject Tracking Based on Active Contour Modeling Abstract: Object Tracking based on Active Contour Modeling is an image processing based technology that uses snapshots of the object under consideration to track it via robot in the real world. The objective has been

to implement a unique methodology that employs the pursuing and adapting of contour to the current state of image, and hence track the object. The system can be implemented in drone planes wherein this algorithm can be used to guide the movement of the gun based on the movements of the object, or, in robot games with a slightly more advanced robot. Initially Image Processing is performed to reduce operation complexity and

achieve swift real-time performance. A set of contour-based modeling algorithms is then implemented to ‘actively’ track the subject. Also, relative transformation calculations are made to lock the target via robot, continuously. MATLAB is used to simulate and implement the system and it is tested on field with a ball placed on it and a robot tracking the ball. The

experiments prove that the system successfully detects and tracks the object efficiently in the real world for all horizontal and vertical transitions.

2014

7

MAT1419

Topic: Vision Based Data Extraction of Vehicles in Traffic

Abstract: With the rise in traffic related crimes the need for an efficient automated surveillance system has become of utmost importance. This paper proposes a system to monitor video from traffic cameras and process it in real t ime for storing essential information of the vehicles in traffic. Histogram of Oriented

Gradients (HOG) of extracted frames is used as features for classification (vehicle frame and non vehicle frame). The classifier is designed based on Support Vector Machine (SVM) . The subtracted image acquired from a dynamically updated background image is used to extract the vehicle image for recognition using

trained Artificial Neural Network(ANN). The system is designed to store details like vehicle make, model, color and time of passing the camera in a database (Microsoft Access (MS Access)). Finally the stored details are made available through a Graphical User Interface(GUI) designed using Visual Basic(VB) that will provide an user with the options of selecting a time window to look for the vehicles that have passed

within that interval or to enter a car model to check if it has passed that point at any time. The system is modeled in MATLAB and tested in a real t ime environment in one of the busiest road in Kamrup district of Assam and provides satisfactory performance.

2014

Topic: Digital Right Management Control for Joint Ownership of Digital Images using Biometric

NEXGEN TECHNOLOGY




8 MAT1418 Features Abstract: This paper proposes a method to establish joint ownership of digital images by embedding imperceptible digital pattern in the image. This digital pattern is generated from biometric features of more than one subject in a strategic matter so that the identification of individual subject can be done and the multiple ownership of the digital images can be established. This digital pattern was embedded and extracted

from the image and the experiments were also carried out when the image was subjected to signal processing attacks. Coefficients of mid frequency band discrete cosine transform was used for embedding as these coefficients do not adversely affect the perceptual transparency and is also significantly robust to normal signal processing attacks. Experimental results indicate that the insertion of this digital

pattern does not change the perceptual properties of the image and the pattern survives signal processing attacks which can be extracted for unique identification.

2014

9

MAT1417

Topic: Intelligent Water Metering System: An Image Processing Approach (MATLAB simulations)

Abstract: The scarcity and misuse of fresh water pose a serious and growing threat to sustainable development. The population growth, severe droughts and uneven distribution of water resources are the reasons for water scarcity, and this scarcity will only continue to grow more severe. The technical

sophistication of meters for measuring water flows has increased noticeably in recent decades in order to improve management of water. This paper proposes simple image processing approach for an intelligent metering system. The proposed system uses simple image processing algorithms and DSP processor, capable of executing MIPS; which makes whole system respond faster. As meter image is being captured from set

distance, meter mask generation reduces the need of algorithms for detection and segmentation of meter reading. The proposed system improves the efficiency of drinking water management and reduces power consumption as image sensor is activated as per predefined billing cycle.

2014

10

MAT1416

Topic: Fingerprint Recognition Using Gabor Filter Abstract: Fingerprint recognition is the most popular methods used for identification with higher degree of success. The fingerprint has unique characteristics called minutiae, which are points where a curve track

finishes, intersect or branches off. In this work a method for Fingerprint recognition is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters for enhancing the image. The proposed method involves combination of Gabor filter and Frequency domain filtering for enhancing the fingerprint.

With eight different orientations of Gabor filter, features of the fingerprint extracting are combined. In Frequency domain filtering, the fingerprint image is subdivided into 32*32 small frames. Features are extracted from these frames in frequency domain. Final enhanced fingerprint is obtained with the results of Gabor filter and frequency domain filtering. Binarization and Thinning follows next

where the enhanced fingerprint is converted into binary and the ridges are thinned to one pixel width. This helps in extracting the Minutiae parts (ridge bifurcation and ridge endings). The overall recognition rate for the proposed method is 95% which is much better than histogram method where the recognition rate is 64%. This project is implemented in MATLAB.

2014

11

MAT1415

Topic: ARIMA Model based Breast Cancer Detection and Classification through Image Processing Abstract: Computer Aided Diagnosis (CAD) has changed the way of medical diagnostics. As similar to

other walk of diagnostics field, CAD is having high potential in breast cancer prognosis because of its highest accuracy. CAD may play a very important role in developing countries i.e. EIT -MEM (Electrical Impedance Tomography –Multi-frequency Electrical Impedance Mammography) device being used for breast cancer defection. MEM-EIT produces tomography based mammograms which are

considered most reliable method of early detection of breast cancer. Cancer diagnostic expert all over the world find this noninvasive technique very accurate as it is one dimensional representation of images in terms of temperature however the accuracy is limited and investigator fail to take into account the spatial co-

ordination between the pixels which is crucial in cancerous tumour detection and their classification

2014

NEXGEN TECHNOLOGY




(cancerous or normal) in EIT (Electrical Impedance Tomography) - based mammogram images. In this study, we are trying to focus an algorithms based CAD (Computer Aided Diagnosis) model for tumour

detection and classification. We model it by ARIMA model (autoregressive integrated moving average (ARIMA) model) and parameter estimation will be performed using leassquare method. Our system classifies the tumour into three categories- (i) healthy tissue (ii) benign tissue (iii) cancerous tissue along with above three segments the performance analysis

between 2D image and 1D image will be done for better accuracy and sensitivity detection.

12

MAT1414

Topic: Human Hand Image Analysis Extracting Finger Coordinates and Axial Vectors Abstract: This paper presents a finger cut -off algorithm for accurate calculation of fingertip coordinates based on hand contours. It provides not only information on exact fingertip position but also orientation and lengths of all fingers in the image. Algorithm can be used for development of user interfaces based on human gesture analysis, such as Touch Table, multimodal gesture based user interface developed by the author.

Advantages of proposed algorithm over fingertip detection algorithm originally used in Touch Table are described.

2014

13

MAT1413

Topic: Automatic Brain Tumor Detection and Segmentation in MR Images

Abstract: The MRI or CT scan images are primary follow up diagnostic tools when a neurologic exam indicates a possibility of a primary or metastatic brain tumor existence. The tumor tissue mainly appears in brighter colors than the rest of the regions in the brain. Based on this observation, an automated algorithm

for brain tumor detection and medical doctors’ assistance in facilitated and accelerated diagnosis procedure has been developed and initially tested on images obtained from the patients with diagnosed tumors and healthy subjects.

2014

14

MAT1412

Topic: RGB ratios based skin detection Abstract: Many different applications like face/people detection, image content interpretation, de-identification for privacy protection in multimedia content, etc. requires skin detection as a pre -

processing step. There is no a perfect solution for skin detection, since this process is a compromise on speed, simplicity and precision (detection quality). There are many different techniques for skin detection modeling ranging from simple models based on one or several thresholds to advanced models based on neural network, Bayesian classifier, maximum entropy, k-means clustering, etc. This

paper proposes a simple model, based on ratios of red, green and blue components of the RGB color model. It describes how to make a compromise in a skin detection modeling by using three levels of rules. Data analysis that supports conclusions is performed on the dataset from Universidad de Chile

(UChile, dbskin2 –complete set) that contains 103 images and t heir annotations.

2014

15

MAT1411

Topic: Embedding of Sound Clips as a Watermark in StilI Images using Discrete Wavelet Transform Abstract: Embedding uj’sndkr im+ys in Iarger images ming the oppmach of watermarking is being efecfively iised ,for image scvutinv. Wirh che advent of digital image processing; secure addition of wutwmah in digitized images ming varivirs techtiiqzies has evolved, The me of wavelet transform for the said pz~ipose has pw ved wry usefit/. This puper presenis a preliminary research carried out to embed audio

clips in still images. The technique uses audio puperties aiidfirral disrortiun tfrreshold in the furget image us parameters-for decision moking,fiw various aspects of the iinplemenfed scheme. Some of these decisions ure selection oJ either grav scale or color images, decomposition level for the wavelet tmnsfbrni, chanvlel selection, sound sample and synrhwis of [he sound sample into minsamples. The research i.y

being exfended ,fbr embedding of audio samples in image sequences for video transmissions jbr .secwe artdio commzrnication applicalions

2014

NEXGEN TECHNOLOGY




16

MAT1410

Topic: Automatic brain tumor detection and segmentation for MRI using covariance and

geodesic distance Abstract: In this paper, we present a new approach that allows the detection and segmentation of brain tumors automatically. The approach is based on covariance and geodesic distance. The

detection of central coordinates of abnormal t issues is based on the covariance method. These coordinates are used to segment the brain tumor area using geodesic distance for T l and T2 weighted magnetic resonance images (MRI). The ultimate objective is to retrieve the attributes of the tumor observed on the image to use them in the step of segmentation and classification. The present

methods are tested on images of Tl and T2 weighted MR and have shown a better performance in the analysis of biomedical images.

2014

17

MAT1409

Topic: ANALYSIS OF RETINAL BLO OD VESSELS USING IMAGE PROCESSING

TECHNIQ UES Abstract: Assessment of blood vessels in human eye allows earlier detection of eye diseases such as

glaucoma and diabetic retinopathy. Digital image processing techniques play a vital role in retinal blood vessel detection , Several image processing methods and filters are in practise to detect and extract the attributes of retinal blood vessels such as length ,width, pattern and angles. Automated Digital image processing techniques and methods has to undergo more of improvisation to achieve precise accuracy to

study the condition of Retinal Vessels especially in cases of Glaucoma and retinopathy; we have explained various Templates based matched filters, Thresholding Methods, Segmentation methods, and functional approaches to isolate the blood vessels.

2014

18

MAT1408

Topic: Automatic Optic Disc Detection in Digital Fundus Images Using Image Processing Abstract: Optic disc (OD) is an important part of the eye. OD detection is an important step in developing systems for automated diagnosis of various serious ophthalmic diseases like Diabetic

retinopathy, Glaucoma, hypertension etc. The variation of intensity within the optic disc and intensity close to the optic disc boundary are the major hurdle in automated optic disc detection. General edge detection algorithms are frequently unsuccessful to segment the optic disc because of this. Complexity increases due to the presence of blood vessels. This paper presents simple method for OD segmentation

by using techniques like principal component analysis (PCA), mathematical morphology and Watershed Transform. PCA used for good presentation of input image and mathematical morphology is used to remove blood vessels from image. Watershed Transform is used for boundary segmentation.

2014

19

MAT1407

Topic: A Comparative Analysis of Edge and Color Based Segmentation for Orange Fruit Recognition Abstract: In this paper, we presented two segmentation methods. Edge based and color based detection methods were used to segment images of orange fruits obtained under natural lighting conditions. Twenty digitized images of orange fruits were randomly selected from the Internet in order to find an orange in each image and to determine its location. We compared the results of

both segmentation results and the color based segmentation outperforms the edge based segmentation in all aspects. The MATLAB image processing toolbox is used for the computation and comparison results are shown in the segmented image results.

2014

20

MAT1406

Topic: Detection of Leukemia in Microscopic Images Using Image Processing Abstract: Leukemia occurs when lot of abnormal white blood cells produced by the bone marrow. Hematologist makes use of microscopic study of human blood, which leads to need of

methods, including microscopic color imaging, segmentation, classification and clustering that can allow identification of patients suffering from Leukemia. The microscopic images will be inspected visually by hematologists and the process is t ime consuming and tiring. The automatic image

2014

NEXGEN TECHNOLOGY




processing system is urgently needed and can overcome related constraints in visual inspection.The proposed system will be on microscopic images to detect Leukemia. The early and fast identification

of Leukemia greatly aids in providing the appropriate treatment. Initial segmentation is done using Statistical parameters such as mean, standard deviation which segregates white blood cells from other blood components i.e. erythrocytes and platelets. Geometrical features such as area, perimeter of the white blood cell nucleusis investigated for diagnostic prediction of

Leukemia. The proposed method is successfully applied to a large number of images, showing promising results for varying image quality. Different image processing algorithms such as Image Enhancement, Thresholding, Mathematical morphology and Labelling are implemented using LabVIEW and MATLAB.

21

MAT1405

Topic: Lung Cancer Diagnosis Using CT-Scan Images Based on Cellular Learning Automata Abstract: Lung cancer has killed many people in recent years. Early diagnosis of lung cancer can help

doctors to treat patients and keep them alive. The most common way to detect lung cancer is using the Computed Tomography (CT) image. The systems that are created by the integration of computers and medical science are called Computer Aided Diagnosis (CAD). A CAD system that is adopted for the

diagnosis lung cancer, uses lung CT images as input and based on an algorithm helps doctors to perform an image analysis. With the help of CAD, doctors can make the final decision. This paper is a study concerning automatic detection of lung cancer by using cellular learning automata. Images include some unwanted data and some feature that are important for processing; pre-processing improves images by removing distortion

and enhance the important features. This system used lung CT scan so we applied some pre-processing method such as Gabor filter and region growing to improve CT images. After pre-processing step according features the lung cancer nodule was extracted. The obtained image through previous steps was entered to cellular learning automata lattice for training and making them

possess the ability to detect lung cancer. The obtained results show, the proposed approach can reduce the error rate.

2014

22

MAT1404

Topic: Image Processing Based Vehicle Detection and Tracking Method Abstract: Vehicle detection and tracking plays an effective and significant role in the area of traffic

surveillance system where efficient traffic management and safety is the main concern. In this paper, we discuss and address the issue of detecting vehicle / traffic data from video frames. Although various researches have been done in this area and many methods have been implemented, still this area has room

for improvements. With a view to do improvements, it is proposed to develop an unique algorithm for vehicle data recognition and tracking using Gaussian mixture model and blob detection methods. First, we differentiate the foreground from background in frames by learning the background. Here, foreground detector detects the object and a binary computation is done to define rectangular regions around every

detected object. To detect the moving object correctly and to remove the noise some morphological operations have been applied. Then the final counting is done by tracking the detected objects and their regions. The results are encouraging and we got more than 91% of average accuracy in detection and tracking using the Gaussian Mixture Model and Blob Detection methods.

2014

23

MAT1403

Topic: Image Encryption Based On Diffusion Process And Multiple Chaotic Maps Abstract: In the modern world, security is a prime important issue and encryption is one of the preeminent

way to ensure security. There are many image encryption schemes. Each one of them has its own strength and weakness. This project presents a novel algorithm for the image encryption and decryption scheme. The project provides a secured image encryption technique using multiple chaotic based circular mapping. In this, first , a pair of sub keys is given by using chaotic logistic maps. Second, the image is encrypted using

logistic map sub key and its transformation leads to diffusion process. Third, sub keys are generated by four different chaotic maps. Based on the initial conditions, each map may produce various random numbers from various orbits of the maps. Among those random numbers,

a particular number are selected as a key for the encryption algorithm. Based on the key, a binary sequence is

2014

NEXGEN TECHNOLOGY




generated to manage the encryption algorithm. The input image of 2-D is transformed into a 1- D array by using raster scanning. It is then divided into various sub blocks. Then the position permutation is applied to

each binary matrix based on multiple chaotic maps. Finally the receiver uses the same sub keys to decrypt the encrypted images. Also using the same encryption and decryption algorithm video is encrypted and decrypted. Finally shown that video encryption and decryption takes more time. Histogram analysis, correlation analysis are also done and found that there is no statistical similarity between original and

encrypted image. Peak Signal to Noise ratio is also calculated and found that the encrypted image is of higher quality.

24

MAT1402

Topic: Real-time Vehicle Color Identification for Surveillance Videos Abstract: Vehicles are one of the main detection targets of the traffic and security video surveillance system. In this paper, we propose an automatic vehicle color identification method for vehicle classification. The main idea of the proposed scheme is to divide a vehicle into a hierarchical coarse-to-fine structure

to extract its wheels, windows, main body, and other auto parts. In the proposed method, the main body alone is used by a support vector machine (SVM) for classification. Experimental results show that the proposed scheme is efficient and effective and the proposed vehicle color identification is suitable for

real-time surveillance applications.

2014

25

MAT1401

Topic: Intelligent Water Metering System: An Image Processing Approach (MATLAB simulations) Abstract : The scarcity and misuse of fresh water pose a serious and growing threat to sustainable development. The population growth, severe droughts and uneven distribution of water resources are the reasons for water scarcity, and this scarcity will only continue to grow more severe. The technical sophistication of meters for measuring water flows has increased noticeably in recent decades in order to

improve management of water. This paper proposes simple image processing approach for an intelligent metering system. The proposed system uses simple image processing algorithms and DSP processor, capable of executing MIPS; which makes whole system respond faster. As meter image is being captured from set distance, meter mask generation reduces the need of algorithms for detection and segmentation of meter

reading. The proposed system improves the efficiency of drinking water management and reduces power consumption as image sensor is activated as per predefined billing cycle.

2014

BULK IEEE PROJECTS IN MATLAB ,BULK IEEE PROJECTS, IEEE 2015-16 MATLAB PROJECTS IN CHENNAI, 2015-16...

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Transcript of BULK IEEE PROJECTS IN MATLAB ,BULK IEEE PROJECTS, IEEE 2015-16 MATLAB PROJECTS IN CHENNAI, 2015-16...