Edge Detection in Noisy Images by Neuro

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  • 8/2/2019 Edge Detection in Noisy Images by Neuro

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    Edge detection in noisy images by neuro-fuzzy processing

    M. Emin Yksel ,a, [Author vitae]

    aDigital Signal and Image Processing Laboratory, Department of Electrical and Electronics

    Engineering, Erciyes University, Kayseri, 38039, Turkey

    Received 21 December 2005.

    Available online 29 March 2006.

    Abstract

    A novel neuro-fuzzy (NF) operator for edge detection in digital images corrupted by

    impulse noise is presented. The proposed operator is constructed by combining a desirednumber of NF subdetectors with a postprocessor. Each NF subdetector in the structure

    evaluates a different pixel neighborhood relation. Hence, the number of NF subdetectors inthe structure may be varied to obtain the desired edge detection performance. Internalparameters of the NF subdetectors are adaptively optimized by training by using simple

    artificial training images. The performance of the proposed edge detector is evaluated on

    different test images and compared with popular edge detectors from the literature.Simulation results indicate that the proposed NF operator outperforms competing edge

    detectors and offers superior performance in edge detection in digital images corrupted by

    impulse noise.

    Keywords:Neuro-fuzzy systems; Image processing; Edge detection

    Article Outline

    1.

    Introduction2.

    Method

    2.1.The proposed neuro-fuzzy operator2.2.The neuro-fuzzy subdetectors

    2.3.The postprocessor

    2.4.Training of the neuro-fuzzy subdetectors

    2.5.Processing of the noisy input image

    3.Results

    4.Discussion and conclusion

    References

    Vitae

    1. Introduction

    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  • 8/2/2019 Edge Detection in Noisy Images by Neuro

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    Edges in a digital image provide important information about the objects contained within

    the image since they constitute the boundaries between the objects in the image. Edge

    detection is a frequently performed operation in many image processing applicationsbecause it is usually the first operation that is performed before other image processing

    tasks such as image segmentation, boundary detection, object recognition and

    classification, image registration, and so on. Consequently, the success of these subsequentimage processing tasks are strictly dependent on the performance of the edge detection

    operation.

    The image intensity shows sudden changes at edges. Therefore, edge detection usually

    involves the calculation of the derivative of the image intensity function at a given pixellocation. If the magnitude of the derivative of the image intensity function is relatively high

    at a given pixel location, then the pixel at that image location is classified as an edge pixel.

    The most important factor decreasing the performance of edge detection is the noise.

    Unfortunately, digital images are inevitably degraded by noise during image acquisition

    and/or transmission due to a number of imperfections encountered in imaging processesand/or communication channels. Most edge detection operators are based on the

    assumption that images contain large homogeneous regions separated by clear boundaries.However, this assumption loses its validity if the image is corrupted by noise. Therefore,

    majority of the edge detection operators require a prefiltering of the noisy image by using

    an appropriate noise filter before edge detection is performed. In this case, however, theperformance of the edge detection operation becomes strictly dependent on the

    performance of the noise filter. Moreover, the complexity of the system and the processing

    time are considerably increased.

    A number of methods for edge detection implementing different approaches to the digital

    calculation of the derivative of the image intensity function are available in the literature.The classical methods [1] and[2] such as the Sobel, Prewitt and Kirsch detectors calculate

    the first directional derivative to determine the locations of the edges. These detectors aresimple to implement but they are usually inaccurate and highly sensitive to noise. The zero-

    crossing edge detectors [1] and[2] use the second derivative along with the Laplacian

    operator. These detectors have fixed detection characteristics in all directions but they are

    very sensitive to noise too. The Gaussian edge detectors[2]reduce the undesirable negativeeffects of noise by smoothing the image before performing edge detection. Hence, they

    exhibit much superior performance over other operators especially in noisy conditions. The

    Canny detector, which is a Gaussian edge detector, is one of the most popular edgedetectors in the literature and it has been widely used in many applications [3], [4] and [5].

    Although the Gaussian detectors exhibit relatively better performance, they are

    computationally much more complex than classical derivative based edge detectors.Furthermore, their performances quickly decrease as the density of the corrupting noise

    increases. Therefore, a novel edge detector that is capable of extracting edges from digital

    images corrupted by noise is highly desirable.

    In the last few years, there has been a growing research interest in the applications of softcomputing based techniques, such as neural networks and fuzzy logic systems, to various

    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  • 8/2/2019 Edge Detection in Noisy Images by Neuro

    3/12

    problems in image processing [6],[7], [8], [9],[10],[11],[12],[13],[14] and[15]. This is

    due mainly to the fact that neuro-fuzzy (NF) systems are very suitable tools to deal with

    uncertainty encountered in the process of extracting useful information from noisy imagessince NF systems combine the ability of neural networks to learn from examples and the

    capability of fuzzy logic systems to model the uncertainty and imprecision. Hence, NF

    systems may be employed as powerful tools for edge detection provided that appropriatenetwork topologies and training strategies are chosen.

    In our recent work, we have shown that a NF system may be utilized to remove impulse

    noise from digital images [16] and[17]; to construct highly efficient hybrid filters for

    restoring noisy digital images while preserving edges, lines and other useful informationwithin the image [18]; to detect noise for guiding switching noise filters and reducing their

    undesirable blurring effects [19] and [20]; and to improve noise suppression and detail

    preservation performances of image filters [21]. In addition to these successfulapplications, we have also shown in a preliminary report [22] that NF systems may be

    employed for efficient detection of edges in noisy digital images.

    In this paper, we extend our preliminary research on edge detection and present a novel NF

    method for edge detection in digital images corrupted by impulse noise. In the proposedmethod, the edges in the noisy image are directly determined by a NF network without

    needing a prefiltering of the noisy input image. The NF network consists of a desired

    number of subdetectors and a postprocessor. Each subdetector evaluates a different pixelneighborhood in the filtering window. The proposed NF edge detector is tested on popular

    images having different image properties and also compared with popular edge detectors

    from the literature. Experimental results show that the proposed NF edge detector exhibits

    much better performance than the competing operators and may efficiently be used for thedetection of edges in digital images corrupted by impulse noise.

    2. Method

    2.1. The proposed neuro-fuzzy operator

    Fig. 1a shows the general structure of the proposed NF edge detection operator. The

    operator is constructed by combining a desired number of NF subdetectors with apostprocessor. All NF subdetectors in the structure operate on the same 3-by-3 pixel

    filtering window, which is shown in Fig. 1b. Each NF subdetector evaluates a different

    neighborhood relation between the center pixel of the filtering window and two of its

    neighbors. Some of the many possible neighborhood topologies are shown in Fig. 1c. The

    higher the number of NF subdetectors, the better the edge detection performance, but thehigher the computational cost.

    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    Full-size image (42K)

    Fig. 1. (a) The general structure of the proposed neuro-fuzzy edge detection operator. Thepixels applied to the inputs of each NF subdetector in the structure are chosen so as to

    utilize the information from a different pixel neighborhood; (b) The filtering window of the

    operator; (c) Some of the possible pixel neighborhood topologies.

    2.2. The neuro-fuzzy subdetectors

    Each NF subdetector is a first-order Sugeno type fuzzy inference system with 3-inputs and

    1-output. The internal structures of the NF subdetectors are identical to each other. Eachinput has 3generalized belltype membership functions and the output has a linearmembership function. The inputoutput relationship of any of the NF subdetectors is asfollows:

    LetX1,X2,X3denote the inputs of the NF subdetector and Ydenote its output. Eachpossible combination of inputs and their associated membership functions is represented by

    a rule in the rule base of the NF subdetector. Since the NF subdetector has 3 inputs andeach input has 3 membership functions, the rule base contains a total of 27 (33) rules, which

    are as follows:

    1. if (X1 isM11) and (X2 isM21) and (X3 isM31), thenR1=F1(X1,X2,X3)

    2. if (X1 isM11) and (X2 isM21) and (X3 isM32), thenR2=F2(X1,X2,X3)

    3. if (X1 isM11) and (X2 isM21) and (X3 isM33), thenR3=F3(X1,X2,X3)

    4. if (X1 isM11) and (X2 isM22) and (X3 isM31), thenR4=F4(X1,X2,X3)

    5. if (X1 isM11) and (X2 isM22) and (X3 isM32), thenR5=F5(X1,X2,X3)

    27. if (X1isM13) and (X2 isM23) and (X3 isM33), thenR27=F27(X1,X2,X3),

    whereMijdenotes thejth membership function of the ith input,Rk denotes the output of thekth rule, andFk denotes the kth output membership function. The input membershipfunctions are generalized bell type

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Noisy Room

Noisy Room

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POWERED BY: THE CUTTING EDGE: Reaching Nonresponders, Neuro-marketing, and Embracing 21st Century Digital Tools to Maximize Engagement #NPPROTGC Bryan.

POWERED BY: THE CUTTING EDGE: Reaching Nonresponders, Neuro-marketing, and Embracing 21st Century Digital Tools to Maximize Engagement #NPPROTGC Bryan.

Noisy images edge detection: Ant colony optimization algorithm

Noisy images edge detection: Ant colony optimization algorithm

Noisy Portfolios

Noisy Portfolios

Centre for Neuro Rehabilitation and Neuro Psychiatry · Centre for Neuro Rehabilitation and Neuro Psychiatry Walkergate Park is a Neuro Rehabilitation and Neuro Psychiatry centre

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Neuro-MEP-4 Delivery Set “Neuro-MEP” amplifier unit Holder “Neuro-MEP” auditory-visual stimulator unit “Neuro-MEP” electrical.

AN ADAPTIVE NEURO-FUZZY SYSTEM FOR … Adaptive Neuro-Fuzzy System for Automatic Image Segmentation and Edge Detection 35 function to be used by the adaptive …

AN ADAPTIVE NEURO-FUZZY SYSTEM FOR … Adaptive Neuro-Fuzzy System for Automatic Image Segmentation and Edge Detection 35 function to be used by the adaptive …

2012 Product Ca talog - A Tension Span · Neuro Passion . Neuro Sleep ; Neuro Bliss . Neuro Sonic . Neuro Sun ; Neuro Trim . Vita CoCo 16.9oz Tetra Pak (12pk) Pure Coconut 1 Liter

2012 Product Ca talog - A Tension Span · Neuro Passion . Neuro Sleep ; Neuro Bliss . Neuro Sonic . Neuro Sun ; Neuro Trim . Vita CoCo 16.9oz Tetra Pak (12pk) Pure Coconut 1 Liter

Detecting Curved Edges in Noisy Images in Sublinear Time · 2017. 11. 28. · J Math Imaging Vis Fig. 2 Curved edge detection on a 1000 × 1000 synthetic noisy image. a Observed noisy

Detecting Curved Edges in Noisy Images in Sublinear Time · 2017. 11. 28. · J Math Imaging Vis Fig. 2 Curved edge detection on a 1000 × 1000 synthetic noisy image. a Observed noisy

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Neuro Assessment for Scalp the Non-Neuro Nurse … · Neuro Assessment for the Non-Neuro Nurse Terry M. Foster, RN, ... Microsoft PowerPoint - Neuro Grand Forks ND [Read-Only] Author:

Neuro Assessment for Scalp the Non-Neuro Nurse … · Neuro Assessment for the Non-Neuro Nurse Terry M. Foster, RN, ... Microsoft PowerPoint - Neuro Grand Forks ND [Read-Only] Author:

Noisy-Channel Coding · Noisy-Channel Coding ... 6?? = p =

Noisy-Channel Coding · Noisy-Channel Coding ... 6?? = p =

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Aspects of Noisy Neural Information Processing · Im Kontext der neuro- ... Special thanks to Lars Schwabe who helped me with getting started with the strange ... 2.3 Wiener Process

Aspects of Noisy Neural Information Processing · Im Kontext der neuro- ... Special thanks to Lars Schwabe who helped me with getting started with the strange ... 2.3 Wiener Process

Noisy Numbers

Noisy Numbers

Noisy Hegselmann-Krause Systems: Phase Transition and the ...chazelle/pubs/noisyHK-JSP.pdf · Noisy Hegselmann-Krause Systems: Phase Transition ... Noisy Hegselmann-Krause Systems

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“Neuro Q no.1 Neuro Quran” - Universiti Sains Malaysiaresearch.kk.usm.my/pages/trans/pdf_part2_2010/Neuro-Q no.1 = Neu… · “Neuro‐Q no.1 = Neuro‐Quran ... Hemorrhagic

“Neuro Q no.1 Neuro Quran” - Universiti Sains Malaysiaresearch.kk.usm.my/pages/trans/pdf_part2_2010/Neuro-Q no.1 = Neu… · “Neuro‐Q no.1 = Neuro‐Quran ... Hemorrhagic

Noisy ad nausea?

Noisy ad nausea?