Abstract. We propose a new approach for perceptual grouping of oriented segments in highly cluttered images based on tensor voting. Segments are represented as second-order tensors and communicate with each other through a voting scheme that incorporates the Gestalt principles of visual perception. An iterative scheme has been devised which removes noise segments in a conservative way using multi-scale analysis and re-voting. We have tested our approach on data sets composed of real objects in real backgrounds. Our experimental results indicate that our method can segment successfully objects in images with up to twenty times more noise segments than object ones.
We developed a self-adaptive genetic algorithm for a maximum likelihood reconstruction of phylogenetic trees using nucleotide sequence data. It resulted in a faster reconstruction of the trees with less computing power arid automatic self-adjustment of settings of the optimization algorithm parameters. We focused on the use of genetic algorithms (GAS) with self-adaptive control parameters arid GAS integration with phvlogenetic tree representations. The developed technique is applicable to any nucleotide sequences inferring evolutionary relationships of organisms.
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