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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