Textural defect detect using a revised ant colony clustering algorithm

Chao Zou; Li Xiao; Bingwen Wang

doi:10.1117/12.748056

15 November 2007 Textural defect detect using a revised ant colony clustering algorithm

Chao Zou, Li Xiao, Bingwen Wang

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880Q (2007) https://doi.org/10.1117/12.748056
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity measurement; the actions of the ants to act next are chosen independently of each other in a stochastic way according to some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of this algorithm. We apply the proposed method in some typical textural images with defects. From the series of pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the defects well.

Citation Download Citation

Chao Zou, Li Xiao, and Bingwen Wang "Textural defect detect using a revised ant colony clustering algorithm", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880Q (15 November 2007); https://doi.org/10.1117/12.748056

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