<title>Fusion of multiscale edge maps for robust segmentation of range images</title>

Abstract

Segmentation is an important process in 3D vision since it is at this stage that all higher level processing begins. Range images are useful because they can provide direct depth measurements of objects in the scene which can be used for navigation and object manipulation tasks. Range images are characterized by two principal types of discontinuities: step edges that represent discontinuities in depth, and roof (or trough) edges that represent orientation discontinuities. A Gaussian weighted least squares technique is developed for extracting these two types of edges from range images. Edge extraction is followed by a surface-based region splitting algorithm in order to generate a more complete partition of the image. The result is fed into a surface-based region growing algorithm which yields the final segmentation image. The algorithm is tested using synthetic and real range image data which illustrate the importance of each of these steps in yielding final segmentation results that are robust and consistent.