An attempt is made to solve the problem of imperfect data produced by state-of-the-art edge detectors through the implementation of laws of perceptual grouping, derived from psychology. A saliency-enhancing operator is introduced. It is capable of highlighting features (edges, junctions, etc.) which are considered important psychologically. It also infers features which are not detected by low-level detectors. It is shown how to extract salient curves and junctions and generate a description ranking these features by the likelihood of them occurring accidentally. The problem of illusory contours apparent in end-point formations is discussed. All operations are parameter-free, noniterative and are linear with the number of edges in the input image.<>


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    Title :

    Inferring global perceptual contours from local features


    Contributors:
    Guy, G. (author) / Medioni, G. (author)


    Publication date :

    1993-01-01


    Size :

    197486 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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