A clustering approach to vector mathematical morphology

The processing and analysis of vector valued signals has become in the last decade a major field of interest. The direct extension of classical processing methods for the scalar signals is not always possible. The mathematical morphology viewed as a processing technique for gray images is such that it cannot be extended easily. In this paper we present an approach to the vector mathematical morphology based on clustering techniques in the signal sample space.

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