Image edge tracking via ant colony optimization

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

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of SPIE: Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 14-16 October 2017, Qingdao, China
PublisherSPIE
Number of pages8
ISBN (Print)9781510617414
DOIs
Publication statusPublished - 2018
EventInternational Conference on Graphic and Image Processing -
Duration: 14 Oct 2017 → …

Publication series

Name
ISSN (Print)0277-786X

Conference

ConferenceInternational Conference on Graphic and Image Processing
Period14/10/17 → …

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