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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 Ima

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-15-M-1944
Agency Tracking Number: F15A-T35-0258
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF15-AT35
Solicitation Number: 2015.1
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-06-01
Award End Date (Contract End Date): 2016-02-29
Small Business Information
6800 Cortona Drive
Goleta, CA 93117
United States
DUNS: 54672662
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Andrew Brown
 Senior Staff Analyst
 (805) 968-6787
Business Contact
 Marcella Lindbery
Title: Mr.
Phone: (805) 968-6787
Research Institution
 The Pennsylvania State University
 Helen Tyson
201 Old Main
University Park, PA 16802
United States

 (814) 863-4020
 Domestic Nonprofit Research Organization

ABSTRACT: 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, including video and hyperspectral 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 in the labeled training data. The proposed algorithmic framework is of a general nature and utility, and will be demonstrated using multiple real-world image and video datasets in Phase I. In Phase II, real-time prototype software will be developed and demonstrated for additional real-world applications, and integration in AFRL systems will be supported.; BENEFIT: The successful completion of this research will result in the development of technology capable of monitoring data from large numbers of disparate imaging and video sensors, with automated or semi-automated recognition and extraction of objects and activities of interest. Work in this proposed effort has the potential to move image/video processing beyond baseline machine vision towards intelligent vision. DoD applications include identification of militarily relevant objects in cluttered scenes containing large numbers of distractor objects, and recognition of threatening activities in the midst of benign activities. Homeland Security and TSA could use this technology to identify potential terrorist threats. Consumer-focused manufactures could develop a new generation of products that interact with and intelligently assist humans in performing a wide range of tasks.

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

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