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MIR: an approach to robust clustering-application to range image segmentation

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2 Author(s)
Koster, K. ; Div. NMS, Nokia Telecommun., Dusseldoft, Germany ; Spann, M.

This paper describes an unsupervised region merging technique based on a novel robust statistical test. The merging decision is derived from the mutual inlier ratio (MIR) of adjacent regions. This ratio is computed using robust regression techniques and a novel method to estimate the robust scale of the Gaussian distribution. A discrimination value to recognize identical Gaussian distributions with the MIR is derived theoretically as a function of the sizes of the compared sets. The presented method to test distributions is compared with the established Kolmogorov-Smirnov test and implemented into a segmentation algorithm for planar range images. The iterative region growing technique is evaluated using an established framework for range image segmentation comparison involving 60 real range images. The evaluation incorporates a comparison with four state-of-the-art algorithms and gives an experimental demonstration of the need for robust methods capable of handling noisy data in real applications

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:22 ,  Issue: 5 )