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Application of Hierarchical Memory Models to Automatic Target Recognition Modeling and Simulation

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8651-18-P-0058
Agency Tracking Number: F18A-014-0148
Amount: $149,996.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF18A-T014
Solicitation Number: 2018.0
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-08-09
Award End Date (Contract End Date): 2019-08-09
Small Business Information
202 Church St. SE. Ste 307
Leesburg, VA 20175
United States
DUNS: 078727222
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Zeeshan Rasheed
 (703) 344-8371
 zrasheed@novateurresearch.com
Business Contact
 Khurram Hassan-Shafique
Phone: (703) 509-0069
Email: kshafique@novateurresearch.com
Research Institution
 University of Massachusetts Amherst
 James Ayres
 
University of Massachusetts Amherst
Amherst, MA 01003
United States

 (413) 577-1594
 Nonprofit College or University
Abstract

This SBIR Phase I project proposes a visual processing system for automated target recognition. Inspired by biological vision systems and hierarchical memory models, the proposed system is capable of learning hierarchical invariant features from unlabeled data that are independent of object labels. The model exploits these learned features to create hierarchical representations of target memories with the ability to learn from a small number of examples. The proposed biologically inspired solutions provide robust target recognition capabilities with missing data, view point changes and illumination variations. This Phase I effort will leverage Novateur Teams expertise in the areas of deep learning and convolutional neural network, top-down and bottom-up visual attention, development of computational memory models, exploitation of remote sensing imagery, and image and scene understanding.

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

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