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Hidden Markov Model (HMM) Topologies for Visual Object Recognition

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: DASG60-01-C-0054
Agency Tracking Number: 00-0269
Amount: $600,000.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Solicitation Year: N/A
Award Year: 2001
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
4295 Okemos Road Suite 100
Okemos, MI 48864
United States
DUNS: 874483704
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Gail Erten
 (517) 349-9000
Business Contact
 Travis Pinter
Title: VP of Business Developmen
Phone: (517) 349-9000
Research Institution

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

* Information listed above is at the time of submission. *

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