The Segmentation of Image Curves

Abstract

IN THIS CHAPTER we will apply the methods of perceptual organization to the difficult but important problem of segmenting image curves. Smoothed, segmented image curves are important perceptual structures in themselves, as well as being needed for the subsequent detection of collinearity, parallelism, connectivity, and other perceptual groupings. Most current edge detectors only detect edge points (image locations through which an edge is judged to pass) and possibly link these together into lists of points on the basis of proximity. The gap between the output of edge-detection techniques and the smoothed, segmented curve descriptions needed for model matching and many perceptual grouping operations is a significant missing link in current image-description methodology. One reason for the difficulty of curve segmentation is that it is actually a combination of several different problems: choosing the best scale of description for a curve, deciding where to place tangent discontinuities (corners), and assigning levels of significance to the final segmentations. This chapter will outline the various requirements that an ideal solution to this problem should satisfy, and will demonstrate a computer program that satisfies most of them.