Edge detection improvement by ant colony optimization

Authors:
De-Sian Lu;Chien-Chang Chen
Affiliations:
Department of Computer Science, Hsuan Chuang University, Hsinchu 300, Taiwan;Department of Computer Science, Hsuan Chuang University, Hsinchu 300, Taiwan
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

Edge detection is a technique for marking sharp intensity changes, and is important in further analyzing image content. However, traditional edge detection approaches always result in broken pieces, possibly the loss of some important edges. This study presents an ant colony optimization based mechanism to compensate broken edges. The proposed procedure adopts four moving policies to reduce the computation load. Remainders of pheromone as compensable edges are then acquired after finite iterations. Experimental results indicate that the proposed edge detection improvement approach is efficient on compensating broken edges and more efficient than the traditional ACO approach in computation reduction.