Triplet-based object recognition using synthetic and real probability models

We describe a model-based object recognition system that uses a probabilistic model for recognizing and locating objects. For each major view class of each 3D object, a probability model consisting of triplets of visible features, their parametrization, and their frequency of detection is constructed from a set of synthetic training images. These synthetic probability models are used to recognize and locate the 3D object from real 2D camera images. The features captured from the real images are then used to create a new, more accurate probability model.