Statistical model for occluded object recognition

In this paper we present a model-based statistical algorithm for recognition of partially occluded objects from noisy features. The likelihood ratio of the image features to template features is used for recognition. Two different statistical occlusion models are introduced: an independent prior model and a Markov random field (MRF) prior model. Our experiments show that the MRF model performs more robustly than the independent model in the presence of partial occlusion.