Edge detection using adaptive thresholding and Ant Colony Optimization

• Published in: 2011 World Congress on Information and Communication Technologies pp. 313 - 318
• Main Authors: Verma, O. P., Singhal, P., Garg, S., Chauhan, D. S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2011
• Subjects: Adaptive thresholding; Ant colony optimization; Detectors; End points; Entropy; Image edge detection; Noise; Optimization; pheromone; Probabilistic logic
• ISBN: 1467301272; 9781467301275
• DOI: 10.1109/WICT.2011.6141264

In this paper, we present an approach for edge detection using adaptive thresholding and Ant Colony Optimization (ACO) algorithm to obtain a well-connected image edge map. Initially, the edge map of the image is obtained using adaptive thresholding. The end points obtained using adaptive threshoding are calculated and the ants are placed at these points. The movement of the ants is guided by the local variation in the pixel intensity values. The probability factor of only undetected neighboring pixels is taken into consideration while moving an ant to the next probable edge pixel. The two stopping rules are implemented to prevent the movement of ants through the pixel already detected using the adoptive thresholding. The results are qualitative analyze using Shanon's Entropy function.