We introduce a confidence measure that estimates the assurance that a graph arc (or edge) corresponds to an object boundary in an image. A weighted, planar graph is imposed onto the watershed lines of a gradient magnitude image and the confidence measure is a function of the cost of fixed-length paths emanating from and extending to each end of a graph arc. The confidence measure is applied to automate the detection of object boundaries and thereby reduces (often greatly) the time and effort required for object boundary definition within a user guided image segmentation environment.


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    Title :

    A confidence measure for boundary detection and object selection


    Contributors:


    Publication date :

    2001-01-01


    Size :

    1309937 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Confidence Measure for Boundary Detection and Object Selection

    Mortensen, E. N. / Barrett, W. A. / IEEE | British Library Conference Proceedings | 2001


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