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Elysium PRO Titles with Abstracts 2017-18
Biometric systems can identify individuals based on their unique characteristics. A new biometric based on hand
synergies and their neural representations is proposed here. In this paper, ten subjects were asked to perform six
hand grasps that are shared by most common activities of daily living. Their scalp electroencephalographic
(EEG) signals were recorded using 32 scalp electrodes, of which 18 task-relevant electrodes were used in feature
extraction. In our previous work, we found that hand kinematic synergies, or movement primitives, can be a
potential biometric. In this paper, we combined the hand kinematic synergies and their neural representations to
provide a unique signature for an individual as a biometric. Neural representations of hand synergies were
encoded in spectral coherence of optimal EEG electrodes in the motor and parietal areas. An equal error rate of
7.5% was obtained at the system’s best configuration. Also, it was observed that the best performance was
obtained when movement specific EEG signals in gamma frequencies (30–50Hz) were used as features. The
implications of these first results, improvements, and their applications in the near future are discussed
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Biometrics Based on Hand Synergies and Their Neural Representations
Face spoofing detection is commonly formulated as a two-class recognition problem where relevant features of
both positive (real access) and negative samples (spoofing attempts) are utilized to train the system. However,
the diversity of spoofing attacks, any new means of spoofing attackers, may invent (previously unseen by the
system) the problem of imaging sensor interoperability, and other environmental factors in addition to the small
sample size make the problem quite challenging. Considering these observations, in this paper, a number of
propositions in the evaluation scenario, problem formulation, and solving are presented. First of all, a new
evaluation protocol to study the effect of occurrence of unseen attack types, where the train and test data are
produced by different means, is proposed. The new evaluation protocol better reflects the realistic conditions in
spoofing attempts where an attacker may come up with new means for spoofing. Inter-database and intra-
database experiments are incorporated into the evaluation scheme to account for the sensor interoperability
problem. Second, a new and more realistic formulation of the spoofing detection problem based on the anomaly
detection concept is proposed where the training data come from the positive class only. The test data, of course,
may come from the positive or negative class. Such a one-class formulation circumvents the need for the
availability of negative training samples, which, in an in deal case, should be the representative of all possible
spoofing types. Finally, a thorough evaluation and comparison of 20 different one-class and two-class systems
on the video sequences of three widely employed databases is performed to investigate the merits of the one-
class anomaly detection approaches compared with the common two-class formulations. It is demonstrated that
the anomaly-based formulation is not inferior as compared with the conventional two-class approach
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An Anomaly Detection Approach to Face Spoofing Detection: A New Formulation
and Evaluation Protocol
Elysium PRO Titles with Abstracts 2017-18
Wristband-placed physical activity monitors, as a convenient means for counting walking steps, assessing
movement, and estimating energy expenditure, are widely used in daily life. There are many consumer-based
wristband monitors on the market, but there is not an unified method to compare their performance. In this paper,
we designed a series of experiments testing step counting performance under different walking conditions to
evaluate these wristband activity monitors. Seven popular brands, including Huawei B1, Mi Band, Fitbit Charge,
Polar Loop, Garmin Vivofit2, Misfit Shine, and Jawbone Up, were selected and evaluated with the proposed
experiment method in this paper. These experiments include four parts, which are walking in a field at a different
walking speed with and without arm swing, walking along a specified complex path, walking on a treadmill,
and walking up and down stairs. Experiment results and analysis with nine healthy subjects were reported to
show the step counting performance of these seven monitors
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Evaluation on Step Counting Performance of Wristband Activity Monitors in Daily
Living Environment
Matching heterogeneous iris images in less constrained applications of iris biometrics is becoming a challenging
task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities
or filtered features. In contrast, this paper proposes a code-level approach in heterogeneous iris recognition. The
non-linear relationship between binary feature codes of heterogeneous iris images is modeled by an adapted
Markov network. This model transforms the number of iris templates in the probe into a homogenous iris
template corresponding to the gallery sample. In addition, a weight map on the reliability of binary codes in the
iris template can be derived from the model. The learnt iris template and weight map are jointly used in building
a robust iris matcher against the variations of imaging sensors, capturing distance, and subject conditions.
Extensive experimental results of matching cross-sensor, high-resolution versus low-resolution and, clear versus
blurred iris images demonstrate the code-level approach can achieve the highest accuracy in compared with the
existing pixel-level, feature-level, and score-level solutions
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A Code-Level Approach to Heterogeneous Iris Recognition
Elysium PRO Titles with Abstracts 2017-18
We propose multi-task and multivariate methods for multi-modal recognition based on low-rank and joint sparse
representations. Our formulations can be viewed as generalized versions of multivariate low-rank and sparse
regression, where sparse and low-rank representations across all modalities are imposed. One of our methods
simultaneously couples information within different modalities by enforcing the common low-rank and joint
sparse constraints among multi-modal observations. We also modify our formulations by including an occlusion
term that is assumed to be sparse. The alternating direction method of multipliers is proposed to efficiently solve
the resulting optimization problems. Extensive experiments on three publicly available multi-modal biometrics
and object recognition data sets show that our methods compare favourably with other feature-level fusion
methods
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Low-Rank and Joint Sparse Representations for Multi-Modal Recognition
Body area networks, including smart sensors, are widely reshaping health applications in the new era of smart
cities. To meet increasing security and privacy requirements, physiological signal-based biometric human
identification is gaining tremendous attention. This paper focuses on two major impediments: the signal
processing technique is usually both complicated and data-dependent and the feature engineering is time-
consuming and can fit only specific datasets. To enable a data-independent and highly generalizable signal
processing and feature learning process, a novel wavelet domain multiresolution convolutional neural network
is proposed. Specifically, it allows for blindly selecting a physiological signal segment for identification purpose,
avoiding the complicated signal fiducial characteristics extraction process. To enrich the data representation, the
random chosen signal segment is then transformed to the wavelet domain, where multiresolution time-frequency
representation is achieved. An auto-correlation operation is applied to the transformed data to remove the phase
difference as the result of the blind segmentation operation. Afterward, a multiresolution 1-D-convolutional
neural network (1-D-CNN) is introduced to automatically learn the intrinsic hierarchical features from the
wavelet domain raw data without data-dependent and heavy feature engineering, and perform the user
identification task. The effectiveness of the proposed algorithm is thoroughly evaluated on eight
electrocardiogram datasets with diverse behaviors, such as with or without severe heart diseases, and with
different sensor placement methods. Our evaluation is much more extensive than the state-of-the-art works, and
an average identification rate of 93.5% is achieved. The proposed multiresolution 1-D-CNN algorithm can
effectively identify human subjects, even from randomly selected signal segments and without heavy feature
engineering. This paper is expected to demonstrate the feasibility and effectiveness of applying the blind signal
processing and deep learning techniques to biometric human identification, to enable a low algorithm
engineering effort and also a high generalization ability.
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Heart ID: A Multi resolution Convolutional Neural Network for ECG-Based
Biometric Human Identification in Smart Health Applications
Elysium PRO Titles with Abstracts 2017-18
Direction information serves as one of the most important features for palmprint recognition. In the past decade,
many effective direction representation (DR)-based methods have been proposed and achieved promising
recognition performance. However, due to an incomplete understanding for DR, these methods only extract DR
in one direction level and one scale. Hence, they did not fully utilize all potentials of DR. In addition, most
researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in
frequency domain. In this paper, we propose a general framework for DR-based method named complete DR
(CDR), which reveals DR by a comprehensive and complete way. Different from traditional methods, CDR
emphasizes the use of direction information with strategies of multi-scale, multi-direction level, multi-region,
as well as feature selection or learning. This way, CDR subsumes previous methods as special cases. Moreover,
thanks to its new insight, CDR can guide the design of new DR-based methods toward better performance.
Motived this way, we propose a novel palmprint recognition algorithm in frequency domain. First, we extract
CDR using multi-scale modified finite radon transformation. Then, an effective correlation filter, namely, band-
limited phase-only correlation, is explored for pattern matching. To remove feature redundancy, the sequential
forward selection method is used to select a small number of CDR images. Finally, the matching scores obtained
from different selected features are integrated using score-level-fusion. Experiments demonstrate that our
method can achieve better recognition accuracy than the other state-of-the-art methods. More importantly, it has
fast matching speed, making it quite suitable for the large-scale identification applications
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Palm print Recognition Based on Complete Direction Representation
Swipe fingerprint scanners (sensors) can be distinguished based on their scanner pattern-a sufficiently unique,
persistent, and unalterable intrinsic characteristic even to scanners of the same technology, manufacturer, and
model. We propose a method to extract the scanner pattern from a single image acquired by a widely-used
capacitive swipe fingerprint scanner and compare it with a similarly extracted pattern from another image
acquired by the same or by another scanner. The method is extremely simple and computationally efficient as it
based on moving-average filtering, yet it is very accurate and achieves an equal error rate below 0.1% for 27
swipe fingerprint scanners of exactly the same model. We also show the receiver operating characteristic for
different decision thresholds of two modes of the method. The method can enhance the security of a biometric
system by detecting an attack on the scanner in which an image containing the fingerprint pattern of the
legitimate user and acquired by the authentic fingerprint scanner has been replaced by another image that may
still contain the fingerprint pattern of the legitimate user but has been acquired by another, unauthentic
fingerprint scanner, i.e., for scanner authentication
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Authentication of Swipe Fingerprint Scanners
Elysium PRO Titles with Abstracts 2017-18
Markov Random Fields (MRFs) are a popular tool in many computer vision problems and faithfully model a
broad range of local dependencies. However, rooted in the Hammersley-Clifford theorem, they face serious
difficulties in enforcing the global coherence of the solutions without using too high order cliques that reduce
the computational effectiveness of the inference phase. Having this problem in mind, we describe a multi-layered
(hierarchical) architecture for MRFs that is based exclusively in pairwise connections and typically produces
globally coherent solutions, with 1) one layer working at the local (pixel) level, modeling the interactions
between adjacent image patches; and 2) a complementary layer working at the object (hypothesis) level pushing
toward globally consistent solutions. During optimization, both layers interact into an equilibrium state that not
only segments the data, but also classifies it. The proposed MRF architecture is particularly suitable for problems
that deal with biological data (e.g., biometrics), where the reasonability of the solutions can be objectively
measured. As test case, we considered the problem of hair / facial hair segmentation and labeling, which are soft
biometric labels useful for human recognition in-the-wild. We observed performance levels close to the state-
of-the-art at a much lower computational cost, both in the segmentation and classification (labeling) tasks
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Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style
Recognition Based on Hierarchic
Faces carry a lot of information to distinguish different individuals. In this context, biometrics-based verification
systems play a major role in terms of recognizing (or confirming) an individual identity, relying on physiological
and/or behavioral characteristics among a set of individual biometric traits. In particular, facial recognition is
important because it has a relatively low cost (i.e., it can be carried out using standard cameras) and is one of
the least intrusive biometric modalities available, since it does not require physical contact like fingerprint
recognition or retina scanning
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Facial biometrics and applicationsal MRFs
Elysium PRO Titles with Abstracts 2017-18
In this paper, a novel joint sparse representation method is proposed for robust face recognition. We embed both
group sparsity and kernelized locality-sensitive constraints into the framework of sparse representation. The
group sparsity constraint is designed to utilize the grouped structure information in the training data. The local
similarity between test and training data is measured in the kernel space instead of the Euclidian space. As a
result, the embedded nonlinear information can be effectively captured, leading to a more discriminative
representation. We show that, by integrating the kernelized local-sensitivity constraint and the group sparsity
constraint, the embedded structure information can be better explored, and significant performance improvement
can be achieved. On the one hand, experiments on the ORL, AR, extended Yale B, and LFW data sets verify
the superiority of our method. On the other hand, experiments on two unconstrained data sets, the LFW and the
IJB-A, show that the utilization of sparsity can improve recognition performance, especially on the data sets
with large pose variation
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Robust Face Recognition with Kernelized Locality-Sensitive Group Sparsity
Representation
A common practice in modern face recognition methods is to specifically align the face area based on the prior
knowledge of human face structure before recognition feature extraction. The face alignment is usually
implemented independently, causing difficulties in the designing of end-to-end face recognition models. We
study the possibility of end-to-end face recognition through alignment learning in which neither prior knowledge
on facial landmarks nor artificially defined geometric transformations are required. Only human identity clues
are used for driving the automatic learning of appropriate geometric transformations for the face recognition
task. Trained purely on publicly available datasets, our model achieves a verification accuracy of 99.33% on the
LFW dataset, which is on par with state-of-the-art single model methods
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Toward End-to-End Face Recognition through Alignment Learning
Elysium PRO Titles with Abstracts 2017-18
In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based
face recognition, where each training and testing example contains a set of face images, which were captured
from different variations of pose, illumination, expression, resolution, and motion. While a variety of feature
learning and dictionary learning methods have been proposed in recent years and some of them have been
successfully applied to image set-based face recognition, most of them learn features and dictionaries for facial
image sets individually, which may not be powerful enough because some discriminative information for
dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice
versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries
simultaneously from raw face pixels so that discriminative information from facial image sets can be jointly
exploited by a one-stage learning procedure. To better exploit the nonlinearity of face samples from different
image sets, we propose a deep SFDL (D-SFDL) method by jointly learning hierarchical non-linear
transformations and class-specific dictionaries to further improve the recognition performance. Extensive
experimental results on five widely used face data sets clearly shows that our SFDL and D-SFDL achieve very
competitive or even better
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Simultaneous Feature and Dictionary Learning for Image Set Based Face
Recognition
This paper addresses the problem of face recognition when there is only few, or even only a single, labeled
examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance
variables, both linear (i.e., additive nuisance variables such as bad lighting, wearing of glasses) and non-linear
(i.e., non-additive pixel-wise nuisance variables such as expression changes). The small number of labeled
examples means that it is hard to remove these nuisance variables between the training and testing faces to obtain
good recognition performance. To address the problem we propose a method called Semi-Supervised Sparse
Representation based Classification (S3RC). This is based on recent work on sparsity where faces are
represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person,
and a variation dictionary representing linear nuisance variables (e.g., different lighting conditions, different
glasses). The main idea is that (i) we use the variation dictionary to characterize the linear nuisance variables
via the sparsity framework, then (ii) prototype face images are estimated as a gallery dictionary via a Gaussian
Mixture Model (GMM), with mixed labeled and unlabeled samples in a semi-supervised manner, to deal with
the non-linear nuisance variations between labeled and unlabeled samples. We have done experiments with
insufficient labeled samples, even when there is only a single labeled sample per person. Our results on the AR,
Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is able to deliver
significantly improved performance over existing methods
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Semi-Supervised Sparse Representation Based Classification for Face Recognition
with Insufficient Labeled Samples
Elysium PRO Titles with Abstracts 2017-18
Face recognition (FR) via regression analysis-based classification has been widely studied in the past several
years. Most existing regression analysis methods characterize the pixelwise representation error via l1-norm or
l2-norm, which overlook the 2D structure of the error image. Recently, the nuclear norm-based matrix regression
model is proposed to characterize low-rank structure of the error image. However, the nuclear norm cannot
accurately describe the low-rank structural noise when the incoherence assumptions on the singular values does
not hold, since it overpenalizes several much larger singular values. To address this problem, this paper presents
the robust nuclear norm to characterize the structural error image and then extends it to deal with the mixed
noise. The majorization-minimization (MM) method is applied to derive a iterative scheme for minimization of
the robust nuclear norm optimization problem. Then, an efficiently alternating direction method of multipliers
(ADMM) method is used to solve the proposed models. We use weighted nuclear norm as classification criterion
to obtain the final recognition results. Experiments on several public face databases demonstrate the
effectiveness of our models in handling with variations of structural noise (occlusion, illumination, and so on)
and mixed noise
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Robust Nuclear Norm-Based Matrix Regression with Applications to Robust Face
Recognition
Heterogeneous face recognition is an important, yet challenging problem in face recognition community. It
refers to matching a probe face image to a gallery of face images taken from alternate imaging modality. The
major challenge of heterogeneous face recognition lies in the great discrepancies between different image
modalities. Conventional face feature descriptors, e.g., local binary patterns, histogram of oriented gradients,
and scale-invariant feature transform, are mostly designed in a handcrafted way and thus generally fail to extract
the common discriminant information from the heterogeneous face images. In this paper, we propose a new
feature descriptor called common encoding model for heterogeneous face recognition, which is able to capture
common discriminant information, such that the large modality gap can be significantly reduced at the feature
extraction stage. Specifically, we turn a face image into an encoded one with the encoding model learned from
the training data, where the difference of the encoded heterogeneous face images of the same person can be
minimized. Based on the encoded face images, we further develop a discriminant matching method to infer the
hidden identity information of the cross-modality face images for enhanced recognition performance. The
effectiveness of the proposed approach is demonstrated (on several public-domain face datasets) in two typical
heterogeneous face recognition scenarios: matching NIR faces to VIS faces and matching sketches to
photographs
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Heterogeneous Face Recognition: A Common Encoding Feature Discriminant
Approach
Elysium PRO Titles with Abstracts 2017-18
The extraction of descriptive features from the sequences of faces is a fundamental problem in facial expression
analysis. Facial expressions are represented by psychologists as a combination of elementary movements known
as action units: each movement is localised and its intensity is specified with a score that is small when the
movement is subtle and large when the movement is pronounced. Inspired by this approach, we propose a novel
data-driven feature extraction framework that represents facial expression variations as a linear combination of
localised basis functions, whose coefficients are proportional to movement intensity. We show that the linear
basis functions of the proposed framework can be obtained by training a sparse linear model with Gabor phase
shifts computed from facial videos. The proposed framework addresses generalisation issues that are not tackled
by existing learnt representations, and achieves, with the same learning parameters, state-of-the-art results in
recognising both posed expressions and spontaneous micro-expressions. This performance is confirmed even
when the data used to train the model differ from test data in terms of the intensity of facial movements and
frame rate
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Learning Bases of Activity for Facial Expression Recognition
Face alignment aims at localizing multiple facial landmarks for a given facial image, which usually suffers from
large variances of diverse facial expressions, aspect ratios and partial occlusions, especially when face images
were captured in wild conditions. Conventional face alignment methods extract local features and then directly
concatenate these features for global shape regression. Unlike these methods which cannot explicitly model the
correlation of neighbouring landmarks and motivated by the fact that individual landmarks are usually
correlated, we propose a deep sharable and structural detectors (DSSD) method for face alignment. To achieve
this, we firstly develop a structural feature learning method to explicitly exploit the correlation of neighbouring
landmarks, which learns to cover semantic information to disambiguate the neighbouring landmarks. Moreover,
our model selectively learns a subset of sharable latent tasks across neighbouring landmarks under the paradigm
of the multi-task learning framework, so that the redundancy information of the overlapped patches can be
efficiently removed. To better improve the performance, we extend our DSSD to a recurrent DSSD (R-DSSD)
architecture by integrating with the complementary information from multi-scale perspectives. Experimental
results on the widely used benchmark datasets show that our methods achieve very competitive performance
compared to the state-of-the-arts
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Learning Deep Sharable and Structural Detectors for Face Alignment
Elysium PRO Titles with Abstracts 2017-18
Significant effort has been devoted within the visual tracking community to rapid learning of object properties
on the fly. However, state-of-the-art approaches still often fail in cases such as rapid out-of-plane rotation, when
the appearance changes suddenly. One of the major contributions of this work is a radical rethinking of the
traditional wisdom of modelling 3D motion as appearance change during tracking. Instead, 3D motion is
modelled as 3D motion. This intuitive but previously unexplored approach provides new possibilities in visual
tracking research. Firstly, 3D tracking is more general, as large out-of-plane motion is often fatal for 2D trackers,
but helps 3D trackers to build better models. Secondly, the tracker’s internal model of the object can be used in
many different applications and it could even become the main motivation, with tracking supporting
reconstruction rather than vice versa. This effectively bridges the gap between visual tracking and Structure
from Motion. A new benchmark dataset of sequences with extreme out-ofplane rotation is presented and an
online leader-board offered to stimulate new research in the relatively underdeveloped area of 3D tracking. The
proposed method, provided as a baseline, is capable of successfully tracking these sequences, all of which pose
a considerable challenge to 2D trackers (error reduced by 46 %)
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TMAGIC: A Model-free 3D Tracker
As more and more stereo cameras are installed on electronic devices, we are motivated to investigate how to
leverage disparity information for autofocus. The main challenge is that stereo images captured for disparity
estimation are subject to defocus blur unless the lenses of the stereo cameras are at the in-focus position.
Therefore, it is important to investigate how the presence of defocus blur would affect stereo matching and, in
turn, the performance of disparity estimation. In this paper, we give an analytical treatment of this fundamental
issue of disparity-based autofocus by investigating the relation between image sharpness and disparity error. A
statistical approach that treats the disparity estimate as a random variable is developed. Our analysis provides a
theoretical backbone for the empirical observation that, regardless of the initial lens position, disparity-based
autofocus can bring the lens to the hill zone of the focus profile in one movement. The insight gained from the
analysis is useful for the implementation of an autofocus system
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Analysis of Disparity Error for Autofocus
Elysium PRO Titles with Abstracts 2017-18
Most existing salient object detection methods compute the saliency for pixels, patches, or superpixels by
contrast. Such fine-grained contrast-based salient object detection methods are stuck with saliency attenuation
of the salient object and saliency overestimation of the background when the image is complicated. To better
compute the saliency for complicated images, we propose a hierarchical contour closure-based holistic salient
object detection method, in which two saliency cues, i.e., closure completeness and closure reliability, are
thoroughly exploited. The former pops out the holistic homogeneous regions bounded by completely closed
outer contours, and the latter highlights the holistic homogeneous regions bounded by averagely highly reliable
outer contours. Accordingly, we propose two computational schemes to compute the corresponding saliency
maps in a hierarchical segmentation space. Finally, we propose a framework to combine the two saliency maps,
obtaining the final saliency map. Experimental results on three publicly available datasets show that even each
single saliency map is able to reach the state-of-the-art performance. Furthermore, our framework, which
combines two saliency maps, outperforms the state of the arts. Additionally, we show that the proposed
framework can be easily used to extend existing methods and further improve their performances substantially
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Hierarchical Contour Closure-Based Holistic Salient Object Detection
Word spotting strategies employed in historical handwritten documents face many challenges due to variation
in the writing style and intense degradation. In this paper, a new method that permits effective word spotting in
handwritten documents is presented that it relies upon document-oriented local features, which take into account
information around representative keypoints as well a matching process that incorporates spatial context in a
local proximity search without using any training data. Experimental results on four historical handwritten data
sets for two different scenarios (segmentation-based and segmentation-free) using standard evaluation measures
show the improved performance achieved by the proposed methodology
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Unsupervised Word Spotting in Historical Handwritten Document Images using
Document-oriented Local Features
Elysium PRO Titles with Abstracts 2017-18
Most existing salient object detection methods compute the saliency for pixels, patches or superpixels by
contrast. Such fine-grained contrast based salient object detection methods are stuck with saliency attenuation
of the salient object and saliency overestimation of the background when the image is complicated. To better
compute the saliency for complicated images, we propose a hierarchical contour closure based holistic salient
object detection method, in which two saliency cues, i.e., closure completeness and closure reliability are
thoroughly exploited. The former pops out the holistic homogeneous regions bounded by completely closed
outer contours, and the latter highlights the holistic homogeneous regions bounded by averagely highly reliable
outer contours. Accordingly, we propose two computational schemes to compute the corresponding saliency
maps in a hierarchical segmentation space. Finally, we propose a framework to combine the two saliency maps,
obtaining the final saliency map. Experimental results on three publicly available datasets show that even each
single saliency map is able to reach the state-of-the-art performance. Furthermore, our framework which
combines two saliency maps outperforms the state of the arts. Additionally, we show that the proposed
framework can be easily used to extend existing methods and further improve their performances substantially
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Hierarchical Contour Closure based Holistic Salient Object Detection