Active Contour Model Based on Salient Boundary Point Image for Object Contour Detection in Natural Image

Yan LI  Siwei LUO  Qi ZOU  

IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.11   pp.3136-3139
Publication Date: 2010/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.3136
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
Local Binary Pattern,  active contour model,  image segmentation,  contour detection,  

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This paper combines the LBP operator and the active contour model. It introduces a salient gradient vector flow snake (SGVF snake), based on a novel edge map generated from the salient boundary point image (SBP image). The MDGVM criterion process helps to reduce feature detail and background noise as well as retaining the salient boundary points. The resultant SBP image as an edge map gives powerful support to the SGVF snake because of the inherent combination of the intensity, gradient and texture cues. Experiments prove that the MDGVM process has high efficiency in reducing outliers and the SGVF snake is a large improvement over the GVF snake for contour detection, especially in natural images with low contrast and small texture background.