boundary detection, confidence, user-guided, object selection
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.
Original Publication Citation
E.N. Mortenson, W.A. Barrett "A Confidence Measure for Boundary Detection and Object Selection," Computer Vision and Pattern Recognition 21, Vol. 1, P. 477-484, December, 21.
BYU ScholarsArchive Citation
Barrett, William A. and Mortensen, Eric N., "A Confidence Measure for Boundary Detection and Object Selection" (2001). Faculty Publications. 555.
Physical and Mathematical Sciences
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