A Scoring, Truthing and Registration Toolkit for ATR Evaluation

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5094, Issue , 2003, Pages 91-100

  Stevens, Mark R ^a   Snorrason, Magnús ^a   Jarratt, Mary ^b   Irvine, John ^c  

^a CHARLES RIVER ANALYTICS   (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

^c SCIENCE APPLICATIONS INTERNATIONAL CORPORATION   (United States)

Author keywords

Automatic target recognition; Evaluation; Registration; Scoring; Truthing

Indexed keywords

ALGORITHMS; DATA ACQUISITION; DATA REDUCTION; DEGREES OF FREEDOM (MECHANICS); IMAGE PROCESSING; SYNTHETIC APERTURE RADAR;

SENSOR IMAGES;

AUTOMATIC TARGET RECOGNITION;

EID: 0345376222     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.487049     Document Type: Conference Paper

References (19)

1. Konstantinides, K., The Khoros Software Development Envrionment for Image and Signal Processing. IEEE Transactions on Image Processing, 1994. 3(4): p. 243-252.
2. Mundy, J., The TargetJr Users Guide. 1998.
3. Mundy, J.L. and IUECommittee. The Image Understanding Envronment: Overview. in Image Understanding Workshop. 1993. Washington, DC: DARPA.
4. Keydel, E.R., S.W. Lee, and J.T. Moore. MSTAR Extended Operating Conditions: A Tutorial, in International Society for Optical Engineering (SPIE). 1996.
5. Ross, T., et al. Extensibility and Other Model-Based ATR Evaluation Concepts, in SPIE AeroSense, Algorithms for Synthetic Aperture Radar Imagery IV. 1997. Orlando, FL.
6. Mossing, J.C. and T.D. Ross, An Evaluation of SAR ATR Algorithm Performance Sensitivity to MSTAR Extended Operating Conditions. 1998.
7. Ross, T.D. and J.C. Mossing. The MSTAR Evaluation Methodology. in SPIE's 13th Annual International Symposium on AeroSense, Algorithms for Synthetic Aperture Radar Imagery VI. 1999. Orlando, FL.
8. Ross, T.D., et al. SAR ATR - So What's the Problem? - An MSTAR Perspective. in SPIE's 13th Annual International Symposium on AeroSense, Algorithms for Synthetic Aperture Radar Imagery VI. 1999. Orlando, FL.
9. Irvine, J.M., et al. Evaluation of Fusion-based ATR Technology. in AeroSense. 2002: SPIE.
10. ATRWG, Target Recognizer Definitions and Performance Measures. 1986.
11. ATRWG, Comparison of Statistical Pattern Recognition, Model-Based and Neural Network Approaches for Automatic Target Recognizer. 1991, Automatic Target Recognizer Working Group: Redstone Arsenal, AL.
12. Jones, T.L. Automatic Target Recognizer Working Group (ATRWG). in The International Society for Optical Engineering: Infrared Systems and Components. 1987. Los Angeles, CA: SPIE.
13. Haralick, R.M., et al., Pose estimation from corresponding point data. IEEE Trans. Sys. Man. Cybernetics, 1989. 19(6): p. 1426-1446.
14. Shapiro, L. and G. Stockman, Computer Vision. 2001: Prentice Hall.
15. Sonka, M., V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision. 1999: PWS Publishing.
16. Stevens, M.R., et al. Feature Based Target Classification in Laser Radar, in AeroSense. 2002: SPIE.
17. Stevens, M.R. and J.R. Beveridge, Precise Matching of 3D Target Models to Multisensor Data. IEEE Transactions on Image Processing, 1997. 6(1): p. 126-142.
18. Wellfare, M. and K. Norris-Zachery. Characterization of Articulated Vehicles using Ladar Seekers. in AeroSense. 1997. Orlando, FL: SPIE.
19. Arnold, G.D. and I. Weiss. Detection and recognition in LADAR with invariants and covariants. in Aersosense 2001. 2001: SPIE.