Christos Theoharatos

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In this study, a conceptually simple, yet flexible and extendable strategy to contrast two different color images is introduced. The proposed approach is based on the multivariate Wald-Wolfowitz test, a nonparametric test that assesses the commonality between two different sets of multivariate observations. It provides an aggregate gauge of the match(More)
The recent advances in sensor technology, microelectronics and multisensor systems have motivated researchers towards processing techniques that combine the information obtained from different sensors. For this purpose a large number of image fusion techniques [Mukhopadhyay & Chanda, 2001; Pohl & van Genderen, 1998, Tsagaris & Anastassopoulos, 2005; Piella,(More)
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)
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(More)
A novel strategy for color-based image retrieval is introduced. Initially, a vector quantization technique is adopted, based on the application of self-organizing neural networks. The color content in each image is summarized by representative RGB-vectors extracted using the Neural-Gas network, an efficient way to extract faithful representations from(More)