Segmentation of range and intensity image sequences by clustering

Abstract

Presents a method for segmenting temporal sequences of range and intensity images. The paper addresses two problems: fusion of intensity and range data for image segmentation, and visual tracking of segments over time. Our method is based on clustering in a 4D feature space which contains intensity and geometric features. The problem of tracking segments over time is solved by adaptive image sequence clustering. The main idea is to use the cluster centers of the previous image to initialize clustering for the current image. This link between consecutive clustering steps allows one to track clusters over time without explicit correspondence analysis. First experiments show that our method can successfully segment and track objects independent of their shapes and motions.