Christos Theoharatos

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In this study, the edge detection task in vector-valued images is examined as a clustering problem. Using samples within a data window, the minimal spanning tree (MST) provides the ordering of multivariate observations and facilitates the identification of similar classes. The edge detector parameters like edge strength, type and orientation are(More)
A multiresolution color image segmentation method is presented that incorporates the main principles of region-based and cluster analysis approaches. A multiscale dissimilarity measure in the feature space is proposed that makes use of non-parametric cluster validity analysis and fuzzy C-Means clustering. Detected clusters are utilized to assign membership(More)
In this work, a similarity measure in the feature space is proposed for color retrieval and indexing based on the ''Multivariate Two-Sample Problem''. Color information is extracted via random selection of image pixels from high-density regions. The proposed scheme has a global nature due to its randomness and is easy to implement. It makes uses of the(More)
In the present study, an efficient strategy for retrieving texture images from large texture databases is introduced and studied within a distributional-statistical framework. Our approach incorporates the multivariate Wald-Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate(More)