Adaptive Edge Detection via Image Statistic Features and Hybrid Model of Fuzzy Cellular Automata and Cellular Learning Automata

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, which is a subclass of stochastic cellular learning automata, is to use the learning automata to adjust the state transition probability of stochastic cellular automata. In this paper, we first provide a mathematical framework for cellular learning automata and then study its convergence behavior. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. The numerical results also confirm the theoretical investigations.

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Continuous optimization using a hybrid model of cellular automata and learning automata

Proceedings of Tomsk State University of Control Systems and Radioelectronics ◽

10.21293/1818-0442-2019-22-1-50-54 ◽

2019 ◽

Vol 22 (1) ◽

pp. 50-54

Author(s):

O. O. Evsutin ◽

◽

A. A. Shelupanov ◽

V. D. Babishin ◽

K. A. Sosedko ◽

...

Keyword(s):

Cellular Automata ◽

Hybrid Model ◽

Learning Automata ◽

Continuous Optimization

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Optimized fuzzy cellular automata for synthetic aperture radar image edge detection

Journal of Electronic Imaging ◽

10.1117/1.jei.27.1.013030 ◽

2018 ◽

Vol 27 (01) ◽

pp. 1 ◽

Cited By ~ 20

Author(s):

Mohammad Farbod ◽

Gholamreza Akbarizadeh ◽

Abdolnabi Kosarian ◽

Kazem Rangzan

Keyword(s):

Cellular Automata ◽

Edge Detection ◽

Synthetic Aperture Radar ◽

Synthetic Aperture Radar Image ◽

Radar Image ◽

Synthetic Aperture ◽

Image Edge Detection ◽

Fuzzy Cellular Automata ◽

Image Edge ◽

Aperture Radar

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Edge detection technique by fuzzy logic and Cellular Learning Automata using fuzzy image processing

2013 International Conference on Computer Communication and Informatics ◽

10.1109/iccci.2013.6466130 ◽

2013 ◽

Cited By ~ 12

Author(s):

Dhiraj Kumar Patel ◽

Sagar A. More

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Edge Detection ◽

Learning Automata ◽

Detection Technique ◽

Cellular Learning Automata ◽

Fuzzy Image

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A Novel Approach for Edge Detection in Images Based on Cellular Learning Automata

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2012100105 ◽

2012 ◽

Vol 2 (4) ◽

pp. 51-61 ◽

Cited By ~ 6

Author(s):

Farhad Soleimanian Gharehchopogh ◽

Samira Ebrahimi

Keyword(s):

Image Processing ◽

Edge Detection ◽

Learning Automata ◽

Edge Detector ◽

Noise Elimination ◽

Minimum Period ◽

Detection Algorithms ◽

Novel Approach ◽

Result Show ◽

Cellular Learning Automata

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

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