Model-based active object recognition using MRF matching and sensor planning
This paper presents an active object recognition algorithm using MAP-MRF matching and sensor planning strategy. The matching between the sensed and model object is based on surface properties. A new measure of surface distinguishability is defined for sensor planning. MAP-MRF framework is used for generating matching label set. A measure of confidence of correct match is determined based on the posterior energy. An active object recognition algorithm is used for determining the next viewpoint of the camera if ambiguity exists in the matching result. The next viewpoint is chosen based the surface with highest distinguishability. Experimental results on images under perfect and imperfect segmentation are presented.
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