Abstract: The paper presents a system for the recognition and pose estimation of 3D objects with an emphasis on the segmentation and verification modules. The system is tailored to the analysis of 3D contour images, which are obtained from image sequences of a CCD camera by means of Kalman filtering. In order to reduce the search complexity and the noise sensitivity of the recognition process, the method is built on robust contour-based 2D algorithms for the retrieval of model candidates from the database and the generation of pose hypotheses. These techniques apply because of the previous segmentation of the 3D contour image into plane curve segments that make up boundary lines of plane surface patches. Hypotheses for the object's pose are obtained by pairwise matching of model and image boundaries. The subsequent verification computes the best globally consistent assignment of model and image contours by searching for groups of similar pose hypothesis. Both the segmentation and the verification algorithm are formulated in terms of clustering approaches and realized by use of a common technique for the evaluation of transformation space. With regard to industrial applications most importance has been attached to the modular design of a robust software solution and the experimental performance evaluation. Experimental results obtained from real-world images are presented.!23
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