OBJECT RECOGNITION AND LOCALIZATION VIA POSE CLUSTERING

The general paradigm of pose clustering is discussed and compared to other techniques applicable to the problem of object detection. Pose clustering is also called hypothesis accumulation and generalized Hough transform and is characterized by a 'parallel' accumulation of low level evidence followed by a maxima or clustering step which selects pose hypotheses with strong support from the set of evidence. Examples are given showing the use of pose clustering in both 2D and 3D problems. Experiments show that the positional accuracy of points placed in the data space by a model pose obtained via clustering is comparable to the positional accuracy of the sensed data from which pose candidates are computed. A specific sensing system is described which yields an accuracy of a few millimeters. Complexity of the sensing system is described which yields an accuracy of a few millimeters. Complexity of the pose clustering approach relative to alternative approaches is discussed.