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Segmentation and classification of edges using minimum description length approximation and complementary junction cues
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-9081-2170
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
1997 (English)In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 67, no 1, 88-98 p.Article in journal (Refereed) Published
Abstract [en]

This article presents a method for segmenting and classifying edges using minimum description length (MDL) approximation with automatically generated break points. A scheme is proposed where junction candidates are first detected in a multiscale preprocessing step, which generates junction candidates with associated regions of interest. These junction features are matched to edges based on spatial coincidence. For each matched pair, a tentative break point is introduced at the edge point closest to the junction. Finally, these feature combinations serve as input for an MDL approximation method which tests the validity of the break point hypotheses and classifies the resulting edge segments as either “straight” or “curved.” Experiments on real world image data demonstrate the viability of the approach.

Place, publisher, year, edition, pages
Elsevier, 1997. Vol. 67, no 1, 88-98 p.
Keyword [en]
curve segmentation, minimum description length, junction detection, edge detection, curvature, classification, object recognition, computer vision
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:kth:diva-58583DOI: 10.1006/cviu.1996.0510OAI: diva2:473385

QC 20130423

Available from: 2012-01-05 Created: 2012-01-05 Last updated: 2013-04-23Bibliographically approved

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