Residual Analysis for Range Image Segmentation and Classification

Abstract

This paper presents an algorithm for the segmentation and classification of dense range images of industrial parts. Range images, are unique in that they directly approximate the physical surfaces of a real world 3-D scene. The segmentation of images (range or intensity) is based on edge detection or region growing techniques. The approach presented in this paper segments range images by combining edge detection and region growing techniques. Jump and roof edges are detected using residual analysis. The residual is defined as the absolute value of the difference between the original image and a filtered version. We show that, at an edge, the difference after smoothing has a maxima in the direction perpendicular to the edge for jump and roof edges. The segmented surfaces is then classified into planar, convex, or concave. The classification is done in two steps. The first step utilizes a variation of the Wald-Wolfowitz runs test to classify the surfaces into planar or curved. The second step further classifies each curved surface into convex or concave using a multi-scale residual computation. The performance of the algorithm on a number of industrial parts range images is presented.