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

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 rst detected in a multi-scale 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 classi es the resulting edge segments as either \straight" or \curved". Experiments on real world image data demonstrate the viability of the approach.