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Small Sample Size Semi-Supervised Feature Clustering for Detection and Classification of Objects and Activities in Still and Motion Multi-spectral Imagery

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-16-C-1769
Agency Tracking Number: F15A-T35-0258
Amount: $750,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF15-AT35
Solicitation Number: 2015.0
Solicitation Year: 2015
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-06-22
Award End Date (Contract End Date): 2018-09-17
Small Business Information
6800 Cortona Drive
Goleta, CA 93117
United States
DUNS: 054672662
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Andrew Brown, Ph.D.
 (805) 968-6787
Business Contact
 Marcella Lindbery
Phone: (805) 968-6787
Research Institution
 The Pennsylvania State University
 Helen Tyson
110 Technology Center, 200 Innovation Blvd.
University Park, PA 16802
United States

 (814) 863-4020
 Nonprofit College or University

Toyon Research Corp. and the Penn State Univ. propose research and development of innovative algorithms for classifying objects and activities observed in high-dimensional data extracted from multi-sensor motion imagery. The proposed algorithms include novel feature clustering techniques to enable effective characterization of intra-class and inter-class appearance variations in datasets containing a small number of labeled, and a large number of unlabeled, high-dimensional feature vectors. The proposed development is expected to provide significant improvements in object and activity classification performance, including maximization of the probability of correct classification and minimization of false declaration rates for real-world applications including highly variable clutter and object and activity types not represented amongst the labeled training data. The proposed algorithmic framework is of a general nature and utility, and will be demonstrated for multiple practical applications in Phase II. Integration in AFRL systems will be supported in Phase II, and related transition opportunities will be pursued.

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

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