Aided and automatic target recognition based upon sensory inputs from image forming systems

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 19, Issue 9, 1997, Pages 1004-1019

  Batches, James A ^a   Walters, C P ^a   Buser, Rudolf G ^a   Guenther, B D ^a  

^a NIGHT VISION AND ELECTRONIC SENSORS DIRECTORATE   (United States)

Author keywords

Aided target acquisition; ATR; Atr algorithms; Automatic target recognition; Databases; Image processing; Imaging sensors; Multisensors; Performance metrics; Sensor fusion

Indexed keywords

ALGORITHMS; DATA ACQUISITION; DATABASE SYSTEMS; IMAGE SENSORS; MATHEMATICAL MODELS; MILITARY APPLICATIONS; SENSOR DATA FUSION; SIGNAL TO NOISE RATIO;

AIDED TARGET ACQUISITION; AUTOMATIC TARGET RECOGNITION; CLUTTER PARAMETERS; PERFORMANCE METRICS;

OBJECT RECOGNITION;

EID: 0031233964     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.615449     Document Type: Article

References (52)

1. B. Bhanu and T. Jones, Image Understanding Research for Automatic Target Recognition," IEEE AES Systems Magazine, pp. 15-22, Oct. 1993.
2. S. Chow and T. Jones, Army's FLIR/ATR Evolution Path," Proc. SPIE, vol. 1,050, no. 6,1989.
3. Marr, D., Vision. San Francisco: W.H. Fueman, 1982.
4. W.K. Pratt, Digital Image Processing. New York: John Wiley & Sons, 1978.
5. Y.-T. Zhou and R. Chellappa, Research Notes in Neural Computing Volume 5, Artificial Neural Networks for Computer Vision. New York: Springer-Verlag, 1992.
6. A. Rosenfeld, Survey: Image Analysis and Computer Vision 1996," Computer Vision and Image Understanding, vol. 66, no. 1, pp. 33-93,1997.
7. L.M. Biberman, Perception of Displayed Information. New York: Plenum Press, 1973, pp. 183-187.
8. J.A. Ratches, Static Performance Model for Thermal Imaging Systems," Optical Eng., vol. 15, no. 6, pp. 525-536,1976.
9. C.P. Walters, Remove the Automatic Target Recognition Performance Evaluation Bottleneck: The CNVEO AUTOSPEC Facility," Optical Eng., vol. 30, no. 3, pp. 247-253,1991.
10. L.G. Clark, L.I. Perlovsky, W.H, Schoendorf, C.P. Plum, and T.L. Keller, Evaluation of FLIR Sensors for Automatic Target Recognition Using an Information-Theoretic Approach," JEEE Proc. NAECON, vol. 2, pp. 1,048-1,055,1993.
11. L.G. Clark and J.V. Veiten, Image Characterization for Automatic Target Recognition Algorithm Evaluations," Optical Eng., vol. 30, no. 2, pp. 147-153,1991.
12. K. Augustyn, A New Approach to Automatic Target Recognition," JEEE Trans. AES, vol. 28, no. 1, pp. 105-114,1992.
13. A. Lanterman, M.I. Miller, and D.L. Snyder, Representations of Thermodynamic Variability in the Automated Understanding of FLIR Scenes," Automatic Object Recognition 6, Proc. SPIE, vol. 2,756, pp. 26-37, Apr. 1996.
14. M. Cooper, U. Grenander, M. Miller, and A. Srivastava, Accommodating Geometric and Thermodynamic Variability for Forward-Looking Infrared Sensors," Algorithms for Synthetic Aperture Radar, Proc. SPIE, vol. 3,070,1997.
15. J.R. Pomerantz, Are Complex Visual Features Derived From Simple Ones?" Formal Theories of Visual Perception, E.L.J. Leeuwenberg and H.F.J.M. Buffort, eds. New York: John Wiley & Sons, 1978.
16. J.R. Pomerantz, Perceptual Organization in Information Processing," Perceptual Organization, M. Kubovy and J.R. Pomerantz, eds. Hillsdale, N.J.: Erlbaum, 1981, pp. 141-180.
17. A. Rosenfeld and M. Thornton, Edge and Curve Detection for Visual Scene Analysis," JEEE Trans. Computers, vol. 20, pp. 562569,1971.
18. B.J. Schachter, A. Lev, S.W. Zucker, and A. Rosenfeld, An Application of Relaxation Methods to Edge Reinforcement," JEEE Trans. Systems, Man and Cybernetics, vol. 7, pp. 813-816,1977.
19. D.K.W. Walters, Computer Vision Model Based on Psychophysical Experiments," Pattern Recognition by Humans and Machines, Volume 2: Visual Perception, E.C. Schwab and H.C. Nusbaum, eds. New York: Academic Press, 1986.
20. T. Poggio, Early Vision," Perspectives in Computing, A. Rosenfeld, ed. New York: Academic Press, pp. 190-206,1986.
21. K.C. Marham, Comparison of Segmentation Processes for Object Acquisition in Infrared Scenes," IEE Proc. F, Radar and Signal Proc., vol. 136, no. 1, pp. 13-21, Feb. 1989.
22. K. Fukunaga, Introduction to Pattern Recognition. New York: Academic Press, 1990.
23. K. Fukunaga, M. Keinosuke, and D.M. Hummels, Bayes Error Estimation Using Parzen and K-NN Procedures," JEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9,1987.
24. K. Fukunaga and R.R. Hayes, The Reduced Parzen Classifier," JEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 4, pp. 423-425, Apr. 1989.
25. J. Baras and A. LaVigne, Convergence of a Neural Network Classifier," Proc 29th IEEE Conf. Decision and Control, pp. 1,7351,740, Dec. 1990.
26. Vector Quantization and Signal Processing, A. Gersho and R. Gray, eds. New York: Academic Press, 1991.
27. K.S. Fu, Syntactic Models in Pattern Recognition and Applications," Pattern Recognition in Practice, E.S. Gelsema and L.N. Kanal, eds. Amsterdam: North-Holland, 1980, pp. 303-314.
28. P.J. Burt, Smart Sensing in Machine Vision," Machine Vision: Algorithms, Architectures, and Systems. New York: Academic Press, 1988.
29. D. Nair and J.K. Aggerwal, A Focused Target Segmentation Paradigm," Proc. Fourth European Conf. Computer Vision, vol. 1, pp. 579-588, Cambridge, England, 1996.
30. S.C. Zhu and A. Yuille, Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation," JEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
31. J. Kitrosser, An Evaluation of a Region-Growing Algorithm Performing Detection for Automatic Target Recognition," J. Imaging Science and Technology, vol. 38, no. 4, pp. 311-320, July/Aug. 1994.
32. M.W. Roth, Survey of Neural Network Technology for Automatic Target Recognition," JEEE Trans. Neural Networks, vol. 1, no. 1, pp. 28-43, Mar. 1990.
33. D. Flannery, J. Loomis, M. Milkovich, and P. Kellin, Applications of Binary Phase-Only Correlation to Machine Vision," Optical Eng., vol. 27, no. 4, pp. 309-320,1988.
34. D. Flannery, J. Loomis, and M. Milkovich, Transform-Ratio Ternary Phase-Amplitude Filter Formation for Improved Correlation Discrimination," Applied Optics, vol. 27, no. 19, pp. 4,079-4,083,1988.
35. J.E. Hummel and I. Biederman, Dynamic Binding in a Neural Network for Shape Recognition," Psychological Rev., vol. 99, no. 3, pp. 480-517,1992.
36. I. Biederman, Recognition-by-Components: A Theory of Human Image Understanding," Psychological Rev., vol. 94, no. 2, pp. 115147,1987.
37. D. Nair and J.K. Aggarwal, Hierarchical, Modular Architectures for Object Recognition by Parts," Proc. 13th Int'l Conf. Pattern Recognition, Vienna, pp. A601-A606,1996.
38. D. Nair and J.K. Aggarwal, Robust Automatic Target Recognition in Second Generation FLIR Images," Proc. IEEE Workshop Applications of Computer Vision, pp. 194-201, Sarasota, Fla., 1996.
39. A. Lanterman, M.I. Miller, and D.L. Snyder, The Unification of Detection, Tracking, and Recognition for Millimeter Wave and Infrared Sensors," Radar/Ladar Processing, Proc. SPIE, vol. 2,562, pp. 150-161, July 1995.
40. D. Reago, E. Lloyd, W. Gercken, E. Jones, M. Gahler, and T. Bui, Fusion of 2nd Generation Forward Looking Infrared, Millimeter Wave Radar and High Pulse Rate Laser Sensors for Tactical Target Acquisition," Proc. 19th Army Science Conf., DTIC No. AD6197265, Orlando, Fla., June 1994.
41. Multisensor Fusion for Computer Vision, J.K. Aggarwal, éd. New York: Springer-Verlag, 1991.
42. J.R. Maheshkumar, V. Veesana, S.S. lyengar, and R. Brooks, New Computational Techniques for Complementary Sensor Interpretations in Detection-Localization Systems," Optical Eng., vol. 35, no. 3,1996.
43. U. Grenander, M.J. Miller, and A. Srivastava, Hubert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups," unpublished paper.
44. D.E. Schmieder and M.R. Weathersby, Detection Performance in Clutter With Variable Resolution," JEEE Trans. AES, vol. 19, no. 4, pp. 622-630,1983.
45. M.V. Shirvailkan and M.M. Trivedi, Developing Texture-Based Image Clutter Measures for Object Detection," Optical Eng., vol. 31, no. 2, pp. 2,628-2,639, Dec. 1992.
46. A. Lanterman, M.I. Miller, and D.L. Snyder, Jump-Diffusion Processes for the Automated Understanding of FLIR Scenes," Automatic Object Recognition 4, Proc. SPIE, vol. 2,234, pp. 416-427, Apr. 1994.
47. U. Grenander and M.I. Miller, Representation of Knowledge in Complex Systems," J. Royal Statistical Soc., vol. 56, no. 4, pp. 569603,1994.
48. F. Arman and J.K. Aggarwal, Model-Based Object Recognition in Dense-Range Images-a Review," Comm. ACM, vol. 25, no. 1, pp. 5-43, Mar. 1993.
49. F. Arman and J.K. Aggarwal, CAD-Based Vision: Object Recognition in Cluttered Range Images Using Recognition Strategies," Computer Vision Graphics and Image Processing: Image Understanding }., vol. 58, no. 1, pp. 33-48,1993.
50. J. Verly, R.L. Delanoy, and D.E. Dudgeon, Model-Based System for Automatic Target Recognition From Forward-Looking LaserRadar Imagery," Optical Eng., vol. 31, no. 12, pp. 2,540-2,552, Dec. 1992.
51. N. Nandhakumar and J.K. Aggarwal, Multisensory Computer Vision," Advances in Computers, vol. 34, p. 60,1992.
52. A. Kramer, D. Perschbacher, R. Johnston, and T. Kipp, Relational Template Matching Algorithm for FLIR Automatic Target Recognition," SPIE Symp. Aerospace and Remote Sensing, pp. 29-37,1993.