Segmentation and Classification of Edges Using Minimum Description Length Approximation and Complementary Junction Cues

Authors: Lindeberg T.; Li M.X.

Source: Computer Vision and Image Understanding, Volume 67, Number 1, July 1997 , pp. 88-98(11)

Publisher: Academic Press

Key:

- Free Content

- New Content

- Subscribed Content

- Free Trial Content

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 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.

Language: English

Document Type: Miscellaneous

Affiliations: Department of Numerical Analysis and Computing Science, Royal Institute of Technology, Stockholm, S-100 44, Sweden:

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$54.38 plus tax Refund Policy