Generating segmented meshes from textured color images
Journal of Visual Communication and Image Representation
An Information Theory framework for two-stage binary image operator design
Pattern Recognition Letters
A pattern-oriented specification of gene network inference processes
Computers in Biology and Medicine
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This paper presents a technique that gives a minimal window W for the estimation of a W-operator from training data. The idea is to choose a subset of variables W that maximizes the information observed in a training set. The task is formalized as a combinatorial optimization problem, where the search space is the powerset of the candidate variables and the measure to be minimized is the mean entropy of the estimated conditional probabilities. As a full exploration of the search space requires prohibitive computational effort, some heuristics of the feature selection literature are applied. The proposed technique is mathematically sound and experimental results including binary image filtering and gray-scale texture recognition show its successful performance in practice.