This paper presents a new approach for model-based object recognition with range images by combining morphological feature extraction and geometric hashing. In low-level processing, range images are segmented into 3D-connected surface patches. In middle-level processing, each connected component is processed by using morphological operations to extract the skeletons of high-variation regions. These skeleton points can be viewed as invariant salient feature primitives. In high-level processing, geometric hashing is used to recognize objects. We also use a basis-similarity constraint to reduce the number of spurious hypotheses. Experimental results have shown that the proposed method is effective and has great potential for model-based object recognition using range images.

Keywords: computer vision, object recognition, range image processing, feature extraction, geometric hashing

Retrieve PDF document (200105_01.pdf)

Received August 29, 1998; revised September 30 & December 2, 1999; accepted January 18, 2000.
Communicated by Shing-Tsaan Huang.