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Segmentation and classification of edges using minimum description length approximation and complementary junction cues

Tony Lindeberg and Meng-Xiang Li

Technical report ISRN KTH NA/P--96/01--SE. Department of Numerical Analysis and Computer Science, KTH (Royal Institute of Technology), SE-100 44 Stockholm, Sweden, Jan 1996.

Extended version in Computer Vision and Image Understanding, vol. 67, no. 1, pp. 88--98, 1997.

Shortened versions in:

Abstract

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 multi-scale pre-processing 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.

Keywords: curve segmentation, minimum description length, junction detection, edge detection, curvature, classification, object recognition, computer vision

Full paper: (PDF) (PostScript) (Selected papers from 9th SCIA)

Background and related material: (Junction detection) (General scale selection principle) (Monograph on scale-space theory)

Responsible for this page: Tony Lindeberg