The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Robust clustering with applications in computer vision
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.>
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Citations
Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
Robust regression using iteratively reweighted least-squares.
Holland, P. W., & Welsch, R. E. (1977) · Communications in Statistics - Theory and Methods · doi:10.1080/03610927708827533 · Cited by 2,115
Metadata
- Authors
- Jolion, J.-M., Meer, P., & Bataouche, S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1991
- Volume
- 13
- Issue
- 8
- Pages
- 791-802
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2171124048
- Cited by
- 254
- References
- 3
- FWCI
- 8.37
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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