Abstract
We propose a novel approach to image segmentation, called feature and spatial domain clustering. The method is devised to group pixel data by taking into account simultaneously both their feature space similarity and spatial coherence. The FSD algorithm is practically application independent. It has been successfully tested on a wide range of image segmentation problems, including grey and colour image segmentation, edge and line detection, range data and motion segmentation. In comparison with existing segmentation approaches, the method can resolve image features even if their distributions significantly overlap in the feature space. It can distinguish between noisy regions and genuine fine texture. Moreover, if required, FSD clustering can produce partial segmentation by identifying salient regions only.
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© 1995 Springer-Verlag Berlin Heidelberg
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Matas, J., Kittler, J. (1995). Spatial and feature space clustering: Applications in image analysis. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_293
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DOI: https://doi.org/10.1007/3-540-60268-2_293
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