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29
Combining Cellular Automata and Particle Swarm Optimization for Edge Detection
- International Journal of Computer Applications
, 2012
"... Cellular Automata can be successfully applied in image processing. In this paper, we propose a new edge detection algorithm, based on cellular automata to extract edges of different types of images, using a totalistic transition rule. The metaheuristic PSO is used to find out the optimal and appropr ..."
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Cellular Automata can be successfully applied in image processing. In this paper, we propose a new edge detection algorithm, based on cellular automata to extract edges of different types of images, using a totalistic transition rule. The metaheuristic PSO is used to find out the optimal and appropriate transition rules set of cellular automata for edge detection task. This combination increases the efficiency of the algorithm, and ensures its convergence to an optimal edge as shown in various experiments. Comparisons are made with standard methods (Canny) and other algorithms based on
Evolutionary Design of Edge Detector Using Rule Changing Cellular Automata
- Second World Congress on Nature and Biologically Inspired Computing, in Kitakyushu
"... Abstract — A new design method for Cellular automata (CA) rules are described. We have already proposed a method for designing the transition rules of two-dimensional 256-state CA for grayscale image denoising. The gene expression programming was employed as the learning algorithm, in which the chro ..."
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Abstract — A new design method for Cellular automata (CA) rules are described. We have already proposed a method for designing the transition rules of two-dimensional 256-state CA for grayscale image denoising. The gene expression programming was employed as the learning algorithm, in which the chromosome encodes the transition rule as the expression. The CA designed by the method ran faster than previous methods. In this paper, an improved method for designing the CA based edge detector is proposed. The ground truth for training CA is generated by the Canny edge detector, from which two objective functions are calculated. Both objective functions are optimized by a multi-objective evolutionary algorithm. The rule-changing CA is used to improve the performance. The experimental results showed that rule-changing CA designed by the proposed method have higher performance for edge detection than the ordinary CA. Keywords-image processing; edge detection; cellular automata; evolutionary computation I.
A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model
"... Cellular Automata (CA) are common and most simple models of parallel computations. Edge detection is one of the crucial task in image processing, especially in processing biological and medical images. CA can be successfully applied in image processing. This paper presents a new method for edge dete ..."
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Cellular Automata (CA) are common and most simple models of parallel computations. Edge detection is one of the crucial task in image processing, especially in processing biological and medical images. CA can be successfully applied in image processing. This paper presents a new method for edge detection of binary images based on two dimensional twenty five neighborhood cellular automata. The method considers only linear rules of CA for extraction of edges under null boundary condition. The performance of this approach is compared with some existing edge detection techniques. This comparison shows that the proposed method to be very promising for edge detection of binary images. All the algorithms and results used in this paper are prepared in MATLAB.
A Neuro-Immune Inspired Robust Real Time Visual Tracking System
"... Abstract. We present a novel Neuro-Immune inspired real-time tracking system that is capable of tracking morphing moving targets over nonbenign backgrounds. We have employed ideas from antigen-presenting cells, T-cell interaction, together with cytokine interaction with neural systems. Our experimen ..."
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Abstract. We present a novel Neuro-Immune inspired real-time tracking system that is capable of tracking morphing moving targets over nonbenign backgrounds. We have employed ideas from antigen-presenting cells, T-cell interaction, together with cytokine interaction with neural systems. Our experiments show that the neuro-immune tracking system has the ability to maintain tracking a target even if the target changes shape, or is covered for periods of time by other objects. Keywords: Neuro-Immune inspired, Visual tracking, Morphing target, Non-benign background, Cellular Immune Network (CIN)
Cellular Automata Image Extraction Algorithm for Gestures Recognition 1
"... This paper presents head and hand gestures recognition system for Human Computer Interaction (HCI). Head and Hand gestures are an important modality for human computer interaction. Vision based recognition system can give computers the capability of understanding and responding to the hand and head ..."
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This paper presents head and hand gestures recognition system for Human Computer Interaction (HCI). Head and Hand gestures are an important modality for human computer interaction. Vision based recognition system can give computers the capability of understanding and responding to the hand and head gestures. The aim of this paper is the proposal of real time vision system for its application within a multimedia interaction environment. This recognition system consists of four modules, i.e. capturing the image, image extraction, pattern matching and command determination. If hand and head gestures are shown in front of the camera, hardware will perform respective action. Gestures are matched with the stored database of gestures using pattern matching. Corresponding to matched gesture, the hardware is moved in left, right, forward and backward directions. Hardware is also control by using voice commands. Cellular automata consist of an n dimensional grid/array of cells. Each of these cells can be in one of a finite number of possible states, updated in parallel according to a state transition function. Compared with many existing algorithms for image extraction cellular automata give much faster search space and can find a uniform rule in less time space.
S.: Detection of duplicated image regions using cellular automata
- In: International Conference on Systems, Signals and Image Processing
, 2014
"... Ahstract-A common image forgery method is copy-move forgery (CMF), where part of an image is copied and moved to a new location. Identification of CMF can be conducted by detection of duplicated regions in the image. This paper presents a new approach for CMF detection where cellular automata (CA) a ..."
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Ahstract-A common image forgery method is copy-move forgery (CMF), where part of an image is copied and moved to a new location. Identification of CMF can be conducted by detection of duplicated regions in the image. This paper presents a new approach for CMF detection where cellular automata (CA) are used. The main idea is to divide an image into overlapping blocks and use CA to learn a set of rules. Those rules appropriately describe the intensity changes in every block and are used as features for detection of duplicated areas in the image. Use of CA for image processing implies use of pixels ' intensities as cell states, leading to a combinatorial explosion in the number of possible rules and subsets of those rules. Therefore, we propose a reduced description based on a proper binary representation using local binary patterns (LBPs). For detection of plain CMF, where no transformation of the copied area is applied, sufficient detection is accomplished by ID CA. The main issue of the proposed method is its sensitivity to post-processing methods, such as the addition of noise or blurring. Coping with that is possible by pre-processing of the image using an averaging filter.
Salt and Pepper Noise Reduction by Cellular Automata
"... Abstract: An algorithm and software based on the concept of cellular automata was developed for removing salt and pepper noise efficiently. The paper shows the software programming for developing an algorithm and software called Cellular Automata Image Denoising (CAID) toolkit. This paper presents ..."
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Abstract: An algorithm and software based on the concept of cellular automata was developed for removing salt and pepper noise efficiently. The paper shows the software programming for developing an algorithm and software called Cellular Automata Image Denoising (CAID) toolkit. This paper presents the CAID toolkit using examples and discusses how the CAID toolkit is designed. Matlab code was used to develop a software program for removing salt and pepper noise in gray and color images. We hope the code will help the researchers who are interested in the Image Denoising for research of image processing.
Real-time Stereo Vision Applications 275 X Real-time Stereo Vision Applications
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INTERNATIONAL JOURNAL OF IMAGING SCIENCE AND ENGINEERING (IJISE) Combined Statistical and Structural Approach for Unsupervised Texture Classification
"... Abstract A combined statistical and structural approach have been used in this paper for texture representation. A set of Texture Primitives have been suggested. These primitives are basically tested for the presence of texture by conducting a suitable statistical test called Nair’s test. The set of ..."
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Abstract A combined statistical and structural approach have been used in this paper for texture representation. A set of Texture Primitives have been suggested. These primitives are basically tested for the presence of texture by conducting a suitable statistical test called Nair’s test. The set of universal primitives are labeled as local descriptor and the frequency of occurrences of these primitives is used as the global descriptor, namely Texture Primitive Spectrum for a given texture image. Since the occurrence of primitives and their placement rules uniquely define a texture image, the primitive spectrum is also unique, for a texture image. These spectrums are shown to be effectively used for un supervised texture classification for Brodatz and Vistex data bases of texture images. An average correct classification of 96 % has been obtained. Key-words-Texture- Primitives – Universal set of Primitives – global descriptor- Texture Primitive Spectrum – Un supervised
