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Image Correspondence Figure of Merit (FOM)
Navy SBIR 2016.1 - Topic N161-013 NAVAIR - Ms. Donna Attick - [email protected] Opens: January 11, 2016 - Closes: February 17, 2016 N161-013 TITLE: Image Correspondence Figure of Merit (FOM) TECHNOLOGY AREA(S): Sensors, Weapons ACQUISITION PROGRAM: PMA 201 Program Office The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 5.4.c.(8) of the solicitation. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Develop an algorithm and a software simulation system that can reliably determine and predict the quality of correspondence between two images of different types, modes, sources, or perspectives. DESCRIPTION: The current mission planning capability for image-guided weapons is limited and undocumented. When planning to employ these weapons, images are used to aid in the target acquisition process and to guide the weapon to the target. This is usually accomplished via some method of comparing images acquired prior to launch with images captured by the weapon while approaching the target. Images used during the mission planning phase may be from any of a number of sources and may be screened for potential best or optimal success in aiding the weapon, based upon content, clarity, and/or mode. What is needed is a simulation or method that can compare images and provide a measure or figure of merit (FOM) for the level of correspondence that should be expected, so that the best image can be selected and used. For example, when planning an image-guided weapon mission, operators are currently instructed during the weapon�s mission planning to add or remove features to improve the weapon�s ability to establish a correspondence, but no feedback is given to the operators letting them know if what they are doing is improving the correspondence or not. With the FOM, operators will receive instant feedback on whether the feature they added or removed improved the likelihood of correspondence or not. With the simulation, operators will also be able to simulate the weapon�s end-game performance. This way they will know whether or not this portion of the overall mission will be successful before executing it. When multiple images are available, the mission planning system can use the FOM, i.e., statistical measure, and the simulation to find the image that would give them the highest success rate for the mission. For example, the simulation would only look at the images with a FOM greater than a pre-determined critical value, i.e., images with the most unique features identified, and simulate the weapon�s performance using the planned profile in a design of experiment. This will determine how many times the weapon correlated to within tolerance using each image. Using these results, the simulation would provide the best image needed to achieve mission success. What is needed is a simulation and a FOM to support mission planners of image-guided weapons. Being able to do both would provide a more robust solution operationally, which is what is desired. Also, the simulation with a fly-out capability would be very useful in developing trust in the new capability with mission planners. That is, mission planners will be able to see a simulated performance of the weapon. Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a SECRET Level Facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DSS and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract. PHASE I: Design, develop, and describe the proposed method mathematically and provide a model or MATLAB TM simulation that demonstrates how the FOM is created. Document the proposed method with some results from a few examples. PHASE II: Refine the model or simulation and provide a prototype executable software system that can be used to test many input samples from different sources and sensor types. Develop documentation to support an independent test and evaluation of the proposed capability. Planning to use/test some classified imagery, which would require the developer to have adequate processing capabilities and cleared personnel. Classified imagery would be provided by the Government PHASE III DUAL USE APPLICATIONS: Develop an application programming interface (API) so the simulation can be integrated with and become part of a mission planning system. Provide support for the method or simulation until the Government can assume ownership and responsibility for its deployment and sustainment. Applications to Homeland Security, Law Enforcement. REFERENCES: 1. Mikhail, E. M., Bethel, J. S., & McGlone, J. C., (2001). Introduction to Modern Photogrammetry, John Wiley & Sons, Inc., New York, NY 2. American Society for Photogrammetry and Remote Sensing (2004). Manual of Photogrammetry, ASPRS, Bethesda, MD 3. Linder, W. (2009). Digital Photogrammetry: A Practical Course, Springer-Verlag, Berlin Heidelberg 4. Le Moigne, J. & Netanyahu, N. S. (2011). Image Registration for Remote Sensing, Cambridge University Press 5. Goshtasby, A. A. (2012). Image Registration: Principles, Tools and Methods, Springer-Verlag, London 6. Russakoff, D. (2010). Intensity-based 2D-3D Medical Image Registration: Algorithms and Analysis, VDM Verlag Dr. Muller KEYWORDS: Targeting; imagery; Modality; Correspondence; Registration; Algorithm TPOC-1: 760-939-0044 TPOC-2: 760-939-8274 Questions may also be submitted through DoD SBIR/STTR SITIS website.
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