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KPAC: A kernel-based parametric active contour method for fast image segmentation

TitleKPAC: A kernel-based parametric active contour method for fast image segmentation
Publication TypeJournal Article
Year of Publication2010
AuthorsMishra, A., and A. Wong
JournalIEEE Signal Processing Letters
Volume17
Pagination312 - 315
ISSN1070-9908
Keywordsedge detection, image segmentation, kernel-based parametric active contour approach, KPAC approach, object boundary detection, Pattern Recognition
Abstract

Object boundary detection has been a topic of keen interest to the signal processing and pattern recognition community. A popular approach for object boundary detection is parametric active contours. Existing parametric active contour approaches often suffer from slower convergence rates, difficulty dealing with complex high curvature boundaries, and are prone to being trapped in local optima in the presence of noise and background clutter. To address these problems, this paper proposes a novel kernel-based active contour (KPAC) approach, which replaces the conventional internal energy term used in existing approaches by incorporating an adaptive kernel derived for the underlying image characteristics. Experimental results demonstrate that the KPAC approach achieves state-of-the-art performance when compared to two other state-of-the-art parametric active contour approaches.

DOI10.1109/LSP.2009.2036654