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Classification-Based Probabilistic Modeling of Texture Transition for Fast Line Search Tracking and Delineation
March 2009 (vol. 31 no. 3)
pp. 570-576
Ali Shahrokni, University of Oxford, UK
Tom Drummond, University of Cambridge, UK
François Fleuret, IDIAP Research Institute, Switzerland
Pascal Fua, Ecole Polytechnic Fédérale de Lausanne, Switzerland
We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners which operate on image intensity discriminative features which are defined on small patches and fast to compute. A database which is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator which requires a ribbon like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2-D delineation and fast 3-D tracking and compare its performance with other existing methods for line search boundary detection.

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Index Terms:
Texture, Tracking, Pixel classification, Markov random fields, Edge and feature detection
Ali Shahrokni, Tom Drummond, François Fleuret, Pascal Fua, "Classification-Based Probabilistic Modeling of Texture Transition for Fast Line Search Tracking and Delineation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 570-576, March 2009, doi:10.1109/TPAMI.2008.236
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