Geometrically guided fuzzy C-means clustering of multispectral images

Jacco C. Noordam; Willie H.A.M. van den Broek; Lutgarde Maria Celina Buydens

doi:10.1117/12.441389

25 September 2001 Geometrically guided fuzzy C-means clustering of multispectral images

Jacco C. Noordam, Willie H.A.M. van den Broek, Lutgarde Maria Celina Buydens

Proceedings Volume 4548, Multispectral and Hyperspectral Image Acquisition and Processing; (2001) https://doi.org/10.1117/12.441389
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

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.

Citation Download Citation

Jacco C. Noordam, Willie H.A.M. van den Broek, and Lutgarde Maria Celina Buydens "Geometrically guided fuzzy C-means clustering of multispectral images", Proc. SPIE 4548, Multispectral and Hyperspectral Image Acquisition and Processing, (25 September 2001); https://doi.org/10.1117/12.441389

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