A novel fuzzy system for edge detection in noisy image using bacterial foraging

Verma, Om Prakash; Hanmandlu, M.; Sultania, Ashish Kumar; Parihar, Anil Singh

doi:10.1007/s11045-011-0164-1

Cited by 33 publications

(12 citation statements)

References 29 publications

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“…We also compared the performance of our proposed method with three classical edge detectors, namely, Canny, Roberts, and Sobel, and with one of the recently reported algorithms, namely, bacterial foraging edge detection method given in [33].…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

“…In this approach, the direction of movement of the bacteria decided by probability matrix is computed using derivatives along the possible edge directions. To deal with noisy images, this method is modified in [33] by using fuzzy derivative in place of probabilistic derivative. A reliable and accurate method of edge detection via tuning of parameters in BFA-based optimization has also been proposed recently in [34].…”

Section: Edge Detection Using Evolutionarymentioning

confidence: 99%

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Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

Khunteta

Ghosh

2014

Advances in Fuzzy Systems

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An edge is a set of connected pixels lying on the boundary between two regions in an image that differs in pixel intensity. Accordingly, several gradient-based edge detectors have been developed that are based on measuring local changes in gray value; a pixel is declared to be an edge pixel if the change is significant. However, the minimum value of intensity change that may be considered to be significant remains a question. Therefore, it makes sense to calculate the edge-strength at every pixel on the basis of the intensity gradient at that pixel point. This edge-strength gives a measure of the potentiality of a pixel to be an edge pixel. In this paper, we propose to use a set of fuzzy rules to estimate the edge-strength. This is followed by selecting a threshold; only pixels having edge-strength above the threshold are considered to be edge pixels. This threshold is selected such that the overall probability of error in identifying edge pixels, that is, the sum of the probability of misdetection and the probability of false alarm, is minimum. This minimization is achieved via particle swarm optimization (PSO). Experimental results demonstrate the effectiveness of our proposed edge detection method over some other standard gradient-based methods.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

Section: Edge Detection Using Evolutionarymentioning

confidence: 99%

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

Khunteta

Ghosh

2014

Advances in Fuzzy Systems

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“…In addition, the BFA is able to jump out of the local optimal solution. Therefore, the BFA has shown good adaptability for solving the problems of job shop scheduling [18], robot path planning [19], image processing [20], and high dimensional optimization [21]. In this study, ACA parameters are mapped into a multidimensional space, and a chemotactic operator is used to enable each group of parameters to approach the optimal value and speed up the convergence of each set of parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

Zhu

2016

Mathematical Problems in Engineering

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The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA) and a particle swarm optimization (PSO), and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.

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“…Edge pixels can be detected easily because their values are so different with their adjacent pixels. But in the presence of noise, this simple definition of edge pixels isn't good because the values of noise pixels are very different with adjacent too [16]. Most of edge detectors include three basic steps: Smoothing, Detection and localization.…”

Section: Introductionmentioning

confidence: 99%

“…However, these approaches have weak performance in dealing with images without impulse noise. Another method, based on bio-inspired bacterial foraging (BF) algorithm, have been proposed by Verma and et al [16]. Edge pixels can be extracted by their approach directly in images that are corrupted by impulse noise.…”

Section: Introductionmentioning

confidence: 99%

Using Hierarchical Adaptive Neuro Fuzzy Systems And Design Two New Edge Detectors In Noisy Images

Olyaee¹,

Abasi²,

Yaghoobi³

2013

Journal of Soft Computing and Applications

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One of the most important topics in image processing is edge detection. Many methods have been proposed for this end but most of them have weak performance in noisy images because noise pixels are determined as edge. In this paper, two new methods are represented based on Hierarchical Adaptive Neuro Fuzzy Systems (HANFIS). Each method consists of desired number of HANFIS operators that receive the value of some neighbouring pixels and decide central pixel is edge or not. Simple train images are used in order to set internal parameters of each HANFIS operator. The presented methods are evaluated by some test images and compared with several popular edge detectors. The experimental results show that these methods are robust against impulse noise and extract edge pixels exactly.

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A novel fuzzy system for edge detection in noisy image using bacterial foraging

Cited by 33 publications

References 29 publications

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

Using Hierarchical Adaptive Neuro Fuzzy Systems And Design Two New Edge Detectors In Noisy Images

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