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Standalone Multiple Anomaly Recognition Technique

Award Information
Agency: Department of Homeland Security
Branch: N/A
Contract: HSHQDC-08-C-00149
Agency Tracking Number: 08-1-TA1-CEI1-002
Amount: $149,913.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Solicitation Year: N/A
Award Year: 2008
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
2710 Glasgow Dr
Carlsbad, CA 92010
United States
DUNS: 827430583
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bill Cardoso
 Principal Investigator
 (630) 456-0055
Business Contact
 Bill Cardoso
Title: President
Phone: (630) 456-0055
Research Institution

Threat materials can take many shapes and therefore this software tool must be able to perform anomaly detection instead of specific shape detection. The problem of solely relying on a database of images and a matching algorithm is that objects in the image may be shifted, rotated, or the image quality is too poor for a reliable match. Thus, this proposal focuses on the development of an anomaly recognition algorithm that is able
to achieve low false positive rates even with low quality input images. Our proposed research will develop an
innovative Standalone Multiple Anomaly Recognition Technique (SMART) to determine the presence of contraband in non intrusive inspection (NII) images of trucks and cargo containers. SMART will be able to position in the NII image the location of the potential contraband by using state-of-the-art spectral decomposition analysis techniques to efficiently differentiate the common background of the image against anomalies. These anomalies, in most cases, represent contraband concealed in the cargo.

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

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