This paper proposes 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. To reduce the number of spurious hypotheses, we propose a basis-similarity constraint. Experimental results have shown that the proposed method is effective and has great potential for model-based object recognition using range images.
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