Hidden Markov Model (HMM) Topologies for Visual Object Recognition

This Phase II SBIR project will design and implement visual object recogniton modules based on Hidden Markov Models (HMM). HMM is a technique that has worked very well for speech recognition and genetic discovery. HMM's success with these problems can beattributed to its flexibility and ability to solve two problems at once, namely segmentation and recognition. This is precisely the case with visual object recognition, as well. In the HMM based object recognition technique demonstrated in Phase Iportion of this project, the hypotheses formation and verification steps of traditional object recogniton architectures are merged without a mandate for a priori segmentation: HMM receives a seeet of image features in context, and in response, produces an