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CAIP
1995
Springer

Spatial and Feature Space Clustering: Applications in Image Analysis

10 years 6 months ago
Spatial and Feature Space Clustering: Applications in Image Analysis
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 applicationindependent. It has been successfullytested 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.
Jiri Matas, Josef Kittler
Added 25 Aug 2010
Updated 25 Aug 2010
Type Conference
Year 1995
Where CAIP
Authors Jiri Matas, Josef Kittler
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