Image Edge Tracking via Ant Colony Optimization

Ruowei Li; Hongkun Wu; Shilong Liu; M. A. Rahman; Sanchi Liu; Ngai Ming Kwok

doi:10.1117/12.2303469

10 April 2018 Image Edge Tracking via Ant Colony Optimization

Ruowei Li, Hongkun Wu, Shilong Liu, M. A. Rahman, Sanchi Liu, Ngai Ming Kwok

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061520 (2018) https://doi.org/10.1117/12.2303469
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

A good edge plot should use continuous thin lines to describe the complete contour of the captured object. However, the detection of weak edges is a challenging task because of the associated low pixel intensities. Ant Colony Optimization (ACO) has been employed by many researchers to address this problem. The algorithm is a meta-heuristic method developed by mimicking the natural behaviour of ants. It uses iterative searches to find the optimal solution that cannot be found via traditional optimization approaches. In this work, ACO is employed to track and repair broken edges obtained via conventional Sobel edge detector to produced a result with more connected edges.

Citation Download Citation

Ruowei Li, Hongkun Wu, Shilong Liu, M. A. Rahman, Sanchi Liu, and Ngai Ming Kwok "Image Edge Tracking via Ant Colony Optimization", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061520 (10 April 2018); https://doi.org/10.1117/12.2303469

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