A robust unsupervised algorithm of contour extraction

Contour extraction is key to automatic object detection. Affected by illumination, clutter and occlusion, extracting contours from real images without templates is a difficult task. In order to improve robustness of unsupervised algorithms of contour extraction, a spectral graph based algorithm sear...

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Bibliographic Details
Main Authors: Qi Zou, Siwei Luo, Yaping Huang
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Image edge detection
Information technology
Layout
Lighting
Merging
Noise robustness
Object detection
Stochastic processes
Tensile stress
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Summary:Contour extraction is key to automatic object detection. Affected by illumination, clutter and occlusion, extracting contours from real images without templates is a difficult task. In order to improve robustness of unsupervised algorithms of contour extraction, a spectral graph based algorithm searching in two directions is presented. Compared with homogeneous methods, our algorithm is more robust to noise. The experimental results on Berkley image base also show efficiency of our method.
ISSN:2164-5221
DOI:10.1109/ICOSP.2008.4697341