Contour-Based Object Detection in Range Images

This paper presents a novel object recognition approach based on range images. Due to its insensitivity to illumination, range data is well suited for reliable silhouette extraction. Silhouette or contour descriptions are good sources of information for object recognition. We propose a complete object recognition system, based on a 3D laser scanner, reliable contour extraction with floor interpretation, feature extraction using a new, fast eigen-CSS method, and a supervised learning algorithm. The recognition system was successfully tested on range images acquired with a mobile robot, and the results are compared to standard techniques, i.e., geometric features, Hu and Zernike moments, the border signature method and the angular radial transformation. An evaluation using the receiver operating characteristic analysis completes this paper. The eigen-CSS method has proved to be comparable in detection performance to the top competitors, yet faster than the best one by an order of magnitude in feature extraction time.