Introduction to statistical pattern recognition (2nd ed.)
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Closed-Loop Object Recognition Using Reinforcement Learning
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CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
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Monte Carlo Localization with Mixture Proposal Distribution
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Probabilistic Object Recognition Using Multidimensional Receptive Field Histograms
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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Vision systems for service robotics applications have to cope with varying environmental conditions, partial occlusions, complex backgrounds and a large number of distractors (clutter) present in the scene. This paper presents a new approach targeted at such application scenarios that combines segmentation, object recognition, 3D localization and tracking in a seamlessly integrated fashion. The unifying framework is the probabilistic representation of various aspects of the scene. Experiments indicate that this approach is viable and gives very satisfactory results.