@inproceedings{01011e00f01d48288b174a7261d6e760,
title = "Image edge tracking via ant colony optimization",
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.",
author = "Ruowei Li and Hongkun Wu and Shilong Liu and Rahman, {M. A.} and Sanchi Liu and Kwok, {Ngai Ming}",
year = "2018",
doi = "10.1117/12.2303469",
language = "English",
isbn = "9781510617414",
publisher = "SPIE",
booktitle = "Proceedings of SPIE: Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 14-16 October 2017, Qingdao, China",
note = "International Conference on Graphic and Image Processing ; Conference date: 14-10-2017",
}