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Object Cueing Using Biomimetic Approaches to Visual Information Processing

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-16-C-0028
Agency Tracking Number: N14A-008-0092
Amount: $749,234.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N14A-T008
Solicitation Number: 2014.0
Solicitation Year: 2014
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-12-14
Award End Date (Contract End Date): 2017-04-11
Small Business Information
5266 Hollister Avenue, Suite 229
Santa Barbara, CA 93111
United States
DUNS: 097607852
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jelena Tesic
 Research Staff Member
 (646) 379-6042
Business Contact
 B.S. Manjunath
Title: Technical Point of Contact
Phone: (805) 448-8115
Research Institution
 University of California Santa Barbara
 Miguel Eckstein
Department of Psychology
Santa Barbara, CA 93106
United States

 (805) 893-2255
 Nonprofit College or University

Thousands of years of evolution have produced the human vision system that computers cannot replicate well. Humans are still unsurpassed in their ability to search for objects in visual scenes. To successfully detect objects in cluttered scenes, the human brain is thought to rely on multiple factors: prior probabilities of object occurrence, global scene statistics and object co-occurrence. Machine object detection continues to be one of the hot areas of computer vision, and it attests both to the fundamental importance of the problem and the fact that state-of-the-art algorithms are shy of performing well in practice. We find that the key problems in current FMV data recognition frameworks (speed, accuracy, robustness, clutter, occlusion, change in view) can be overcome by maturing computer vision systems with architectures and components inspired by advances in neuroscience and computational human vision. In Phase I, we have demonstrated a clear path towards the solving small object recognition problem in overhead videos. In Phase II, we will focus on delivering end-to-end biomimetic solution to scene and object recognition with high sensitivity and specificity for a range of overhead video feeds from moving aerial platforms, from fast-moving narrow field sensor feeds to persistent wide area feeds.

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

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