Keywords
boundary detection, confidence, user-guided, object selection
Abstract
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.
https://scholarsarchive.byu.edu/facpub/555
Document Type
Peer-Reviewed Article
Publication Date
2001-12-01
Permanent URL
http://hdl.lib.byu.edu/1877/2608
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/