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Range image segmentation based on split-merge clustering

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2 Author(s)
RiHua Xiang ; Beijing Special Eng. Design Inst., China ; Runsheng Wang

In this paper, we present a split-merge clustering segmentation algorithm based on Gaussian mixture models, which resolves the models by expectation-maximization (EM) algorithm and seeks model via Bayesian information criterion (BIC). It starts iteratively splitting from a single Gaussian model, then iteratively merging clusters. After convergence of the last stage, the clustering model is selected via a modified BIC and used to gain an initial segmentation, followed by a region merge step to achieve final segmentation. New algorithm was applied to 60 range images acquired by two kinds of range cameras, and got approving results with acceptable computation time.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:3 )

Date of Conference:

23-26 Aug. 2004