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Image Edge Tracking via Ant Colony Optimization
Author(s): Ruowei Li; Hongkun Wu; Shilong Liu; M. A. Rahman; Sanchi Liu; Ngai Ming Kwok
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Paper 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.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061520 (10 April 2018); doi: 10.1117/12.2303469
Show Author Affiliations
Ruowei Li, Univ. of New South Wales (Australia)
Hongkun Wu, Univ. of New South Wales (Australia)
Shilong Liu, Univ. of New South Wales (Australia)
M. A. Rahman, Univ. of New South Wales (Australia)
Sanchi Liu, Univ. of New South Wales (Australia)
Ngai Ming Kwok, Univ. of New South Wales (Australia)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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