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<p>A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed. The MVE estimator identifies the least volume region containing h percent of the data points. The clustering algorithm iteratively partitions the space into clusters without prior information about their number. At each iteration, the MVE estimator is applied several times with values of h decreasing from 0.5. A cluster is hypothesized for each ellipsoid. The shapes of these clusters are compared with shapes corresponding to a known unimodal distribution by the Kolmogorov-Smirnov test. The best fitting cluster is then removed from the space, and a new iteration starts. Constrained random sampling keeps the computation low. The clustering algorithm was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation.</p>
statistical analysis; minimum volume ellipsoid robust estimator; iterative methods; constrained random sampling; computer vision; clustering algorithm; unimodal distribution; Kolmogorov-Smirnov test; feature space; multithresholding; gray level images; Hough space; range image segmentation; computer vision; estimation theory; iterative methods; statistical analysis

P. Meer, S. Bataouche and J. Jolion, "Robust Clustering with Applications in Computer Vision," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 791-802, 1991.
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