Fourth IEEE International Conference on Computer Vision Systems (ICVS'06) A Learning Approach for Adaptive Image Segmentation New York, New York January 04-January 07 ISBN: 0-7695-2506-7
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICVS.2006.4
As mentioned in many papers, a lot of key parameters of image segmentation algorithms are manually tuned by de- signers. This induces a lack of flexibility of the segmentation step in many vision systems. By a dynamic control of these parameters, results of this crucial step could be drastically improved. We propose a scheme to automatically select segmentation algorithm and tune theirs key parameters thanks to a preliminary supervised learning stage. This paper details this learning approach which is composed by three steps: (1) optimal parameters extraction, (2) algorithm selection learning, and (3) generalization of parametrization learning. The major contribution is twofold: segmentation is adapted to the image to segment, and in the same time, this scheme can be used as a generic framework, independant of any application domain.
Index Terms:
design methods for vision systems, image segmentation,learning techniques.
Citation:
Vincent Martin, Monique Thonnat, Nicolas Maillot, "A Learning Approach for Adaptive Image Segmentation," icvs, pp.40, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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