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    A Learning Approach for Adaptive Image Segmentation

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    Date
    2007
    Author
    Martin, Vincent
    Thonnat, Monique
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    Abstract
    In this chapter, we have proposed a learning approach for three major issues of image segmentation: context adaptation, algorithm selection and parameter tuning according to the image content and the application need. This supervised learning approach relies on hand-labelled samples. The learning process is guided by the goal of the segmentation and therefore makes the approach reliable for a broad range of applications. The user effort is restrained compared to other supervised methods since it does not require image processing skills: the user has just to click into regions to assign labels, he/she never interacts with algorithm parameters. For the figure-ground segmentation task in video application, this annotation task is even automatic.
    URI
    https://lib.hpu.edu.vn/handle/123456789/22707
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    • Education [806]

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