Uncertainty Propagation in Model-Based Recognition
International Journal of Computer Vision
Non-Invasive Functional Brain Mapping Using Registered Transcranial Magnetic Stimulation
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
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Two formulations of model-based object recognition are described. MAP Model Matching evaluates joint hypotheses of match and pose, while Posterior Marginal Pose Estimation evaluates the pose only. Local search in pose space is carried out with the Expectation--Maximization (EM) algorithm. Recognition experiments are described where the EM algorithm is used to refine and evaluate pose hypotheses in 2D and 3D. Initial hypotheses for the 2D experiments were generated by a simple indexing method: {Angle Pair Indexing}. The Linear Combination of Views method of Ullman and Basri is employed as the projection model in the 3D experiments.