Edge detection using adaptive thresholding and Ant Colony Optimization

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...

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Bibliographic Details
Published in2011 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
LanguageEnglish
Published IEEE 01.12.2011
Subjects
Adaptive thresholding
Ant colony optimization
Detectors
End points
Entropy
Image edge detection
Noise
Optimization
pheromone
Probabilistic logic
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ISBN1467301272
9781467301275
DOI10.1109/WICT.2011.6141264

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Summary: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.
ISBN:1467301272
9781467301275
DOI:10.1109/WICT.2011.6141264