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We extend the visualization technique of high-dimensional patterns conceived by Sammon to the case when the patterns have been previously mapped to an implicitly defined Hilbert feature space in which distances can be measured by kernels. The principal benefit of our technique is the possibility to gain insight into the distribution of the patterns, even in this generally non-accessible feature space.
A method to detect boundaries in in natural color images is here proposed, combining edge information and region information. This unsupervised fully automatic process uses edge map information to eliminate false boundaries in the image region map, and region map information to remove noise in the image edge map. Thus, it integrates these two maps into a single one to get the final result. This proposal...
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