Edge detection with fuzzy cellular automata transition function optimized by PSO. (April 2015)

• Record Type: Journal Article
• Authors: Uguz, S.; Sahin, U.; Sahin, F.
• Is Part Of: Computers & electrical engineering. Volume 43(2015)
• Page Start: 180
• Page End: 192
• Publication Date: 2015-04
• Subjects: Cellular automata -- Image processing -- Edge detection -- Cellular automata linear rules; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.01.017
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Fig. 2. Proposed method result of different rules for Im 1 ( Δ = 110, τ = 0.62 ), (a) Original Image, (b) Ground Truth, (c) Rule 47, (d) Rule 170, (e) Rule 367, (f) Rule 510. Highlights: This study is the application of 2D linear cellular automata (CA) rules with the help of fuzzy membership function to the problems of edge detection. An efficient and simple thresholding technique of edge detection based on CA transition rules optimized by Particle Swarm Optimization method (PSO) is proposed. Results of the proposed to the selected 22 images from the Berkeley Segmentation Dataset (BSDS) are presented. Comparison with some classical Sobel and Canny results is included. Baddeley Delta Metric (BDM) is used for the performance index to compare the results. Abstract: In this paper we discuss the application of two-dimensional linear cellular automata (CA) rules with the help of fuzzy heuristic membership function to the problems of edge detection in image processing applications. We proposed an efficient and simple thresholding technique of edge detection based on fuzzy cellular automata transition rules optimized by Particle Swarm Optimization method (PSO). Finally, we present some results of the proposed linear rules for edge detection to the selected 22 images from the Berkeley Segmentation Dataset (BSDS) and compare with some classical Sobel and Canny results. Also, Baddeley Delta Metric (BDM) is used for the performance index to compare the results.