This paper presents a technique inspired by swarm methodologies such as ant colony algorithms for processing simple and complicated images. It is shown that the proposed technique for image processing is capable of performing feature extraction for edge detection and segmentation, even in the presence of noise. Our proposed approach, Ant-based Correlation for Edge Detection (ACED), is tested on different samples and the results are compared to typical established non-swarm-based methods. The comparative analysis highlights the advantages of the proposed method which generates less distortion when noise is added to the test images. Both qualitative and quantitative evaluations support the claim, confirming the significance of our swarm-based method for image feature extraction and segmentation.

Additional Metadata
Keywords Ant colony systems, Feature extraction, Image edge analysis
Persistent URL dx.doi.org/10.1016/j.asoc.2011.06.011
Journal Applied Soft Computing Journal
Citation
Etemad, S.A. (S. Ali), & White, A. (2011). An ant-inspired algorithm for detection of image edge features. Applied Soft Computing Journal, 11(8), 4883–4893. doi:10.1016/j.asoc.2011.06.011