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Proceedings Paper

Geometrically guided fuzzy C-means clustering of multispectral images
Author(s): Jacco C. Noordam; Willie H.A.M. van den Broek; Lutgarde Maria Celina Buydens
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Paper Abstract

Fuzzy C-means (FCM) is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. The segmentation is based on spectral information only and geometrical relationship between neighboring pixels is not used. In this paper, a semi-supervised FCM technique is used to add geometrical information during clustering. Geometrical information can be adapted from the local neighborhood, or from a more extended shape model such as the hough circle detection. Segmentation experiments with the Geometrically Guided FCM (GG-FCM) show improved segmentation above traditional FCM such as more homogeneous regions and less spurious pixels.

Paper Details

Date Published: 25 September 2001
PDF: 6 pages
Proc. SPIE 4548, Multispectral and Hyperspectral Image Acquisition and Processing, (25 September 2001); doi: 10.1117/12.441389
Show Author Affiliations
Jacco C. Noordam, Agrotechnological Research Institute and Katholieke Univ. Nijmegen (Netherlands)
Willie H.A.M. van den Broek, Agrotechnological Research Institute (Netherlands)
Lutgarde Maria Celina Buydens, Katholieke Univ. Nijmegen (Netherlands)

Published in SPIE Proceedings Vol. 4548:
Multispectral and Hyperspectral Image Acquisition and Processing
Qingxi Tong; Yaoting Zhu; Zhenfu Zhu, Editor(s)

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