Objective X-ray Image Display Evaluation (OXIDE)

Award Information
Agency: Department of Homeland Security
Branch: N/A
Contract: HSHQDC-12-C-00077
Agency Tracking Number: DHS SBIR-2012.2-H-SB012.2-003-0001-I
Amount: $99,993.20
Phase: Phase I
Program: SBIR
Awards Year: 2012
Solicitation Year: 2012
Solicitation Topic Code: H-SB012.2-003
Solicitation Number: DHS SBIR-2012.2
Small Business Information
Charles River Analytics Inc.
625 Mount Auburn Street, Cambridge, MA, 02138-4555
DUNS: 115243701
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Ross Eaton
 (617) 491-3474
 reaton@cra.com
Business Contact
 Mark Felix
Title: Contracts Manager
Phone: (617) 491-3474
Email: mfelix@cra.com
Research Institution
N/A
Abstract
Transportation Security Officers (TSOs) are tasked with exploiting x-ray inspection systems to detect potential threats. To ensure the safety and security of the traveling public and to ensure efficient operation, TSOs must maintain a 100% probability of detection (Pd) rate while minimizing screening time and cost. Achieving these objectives requires that x-ray inspection systems operate predictably in accordance with manufacturer specifications and are calibrated to maximize Pd and reduce screening time. The ASTM X-ray Test Object aims to evaluate image quality (IQ) with respect to Pd in order to support optimal system calibration. However, the ASTM is fundamentally flawed for several reasons: (1) it is prone to operator bias; (2) it is not directly representative of real-world operation; (3) it does not quantify the relationship between IQ and Pd; and (4) it cannot handle moving objects and is therefore ineffective for exploring the use of continuously rotating conveyor belts to speed up the screening process. To address these concerns, we propose a system for Objective X-ray Image Display Evaluation (OXIDE). OXIDE uses a predictive approach to assess functional image quality, implemented using a large bank of image features, an array of test kit item detectors, and a regression algorithm which maps image features to detector performance. To maintain a high Pd, minimize screening time, and improve safety, OXIDE monitors IQ of unseen imagery during normal operation and cues the operator to potential image degradations during calibration.

* information listed above is at the time of submission.

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