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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jolion, J.-M. Meer, P. Bataouche, S. |
| Copyright Year | 1979 |
| Abstract | 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.< |
| Sponsorship | IEEE Computer Society |
| Starting Page | 791 |
| Ending Page | 802 |
| Page Count | 12 |
| File Size | 1455006 |
| File Format | |
| ISSN | 01628828 |
| Volume Number | 13 |
| Issue Number | 8 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1991-08-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robustness Application software Computer vision Clustering algorithms Partitioning algorithms Iterative algorithms Ellipsoids Shape Testing Image sampling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software |
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