Abstract
In this paper, we present a new color image segmentation algorithm which is based on fuzzy mathematical morphology. After a color pixel projection into an attribute space, segmentation consists of detecting the different modes associated with homogeneous regions. In order to detect these modes, we show how a color image can be viewed as a fuzzy set with its associated membership function corresponding to a mode which is defined by a color cooccurrence matrix and by mode concavity properties. A new developed fuzzy morphological transformation is then applied to this membership function in order to identify the modes. The performance of our proposed fuzzy morphological approach is then presented using a test color image, and is then compared to the competitive learning algorithm.
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© 2000 Springer-Verlag Berlin · Heidelberg
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Gillet, A., Botte-Lecocq, C., Macaire, L., Postaire, JG. (2000). Application of Fuzzy Mathematical Morphology for Unsupervised Color Pixels Classification. In: Kiers, H.A.L., Rasson, JP., Groenen, P.J.F., Schader, M. (eds) Data Analysis, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59789-3_11
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DOI: https://doi.org/10.1007/978-3-642-59789-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67521-1
Online ISBN: 978-3-642-59789-3
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