Abstract
A fuzzy set theory based region merging approach is presented to tackle the issue of oversegmentation from the watershed algorithm, for achieving robust image segmentation. A novel hybrid similarity measure is proposed as the merging criterion, based on the region-based similarity and the edge-based similarity. Both similarities are obtained using the fuzzy set theory. To adaptively adjust the influential degree of each similarity to region merging, a simple but effective weighting scheme is employed with the weight varying as region merging proceeds. The proposed approach has been applied to various images, including gray-scale images and color images. Experimental results have demonstrated that the proposed approach produces quite robust segmentations.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhu, H., Basir, O. (2005). Fuzzy Sets Theory Based Region Merging for Robust Image Segmentation. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_55
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DOI: https://doi.org/10.1007/11539506_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
Online ISBN: 978-3-540-31830-9
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