A hybrid approach to edge detection using fuzzy sets and cellular learning automata

Abstract

Edge detection technique has a key role in machine vision and image understanding systems. In machine vision motion track and measurement system based on discrete feature, the exact feature edge orientation in the image is the precondition of the successful completion of the vision measurement task. Edge detection is one of the most commonly used operations in image analysis and digital image processing. Many studies have been conducted to enhance the edge detection algorithms in various domains. In this thesis, a robust edge detection method based on Fuzzy Sets and Cellular Learning Automata (CLA) is proposed. The proposed method includes two steps: (a) extracting the edges and (b) enhancing them by removing unwanted edges and eliminating false edges caused by noise. The performance of the proposed edge detector is tested on various test images with different sizes. The results are compared with Canny and Sobel edge detection methods. Simulation results reveal that the proposed Fuzzy-CLA method can detect edges more smoothly in a shorter amount of time compared to the other edge detectors.