On the limits of target recognition in the presence of atmospheric effects

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6968, Issue , 2008, Pages

  Chen, Xiaohan ^a   Schmid, Natalia A ^a  

^a West Virginia University   (United States)

Author keywords

Atmospheric effects; Automatic target recognition; EO imaging; Target detection

Indexed keywords

(ALGORITHMIC) COMPLEXITY; (E ,3E) PROCESS; APPLIED (CO); ATMOSPHERIC EFFECTS; BESSEL K FORMS; CLUTTER REJECTION; DATA SETS; DEFOCUS BLUR; DIE CAST; DISTORTED IMAGES; EFFICIENT DETECTION; ELECTRO-OPTICAL; ENVIRONMENTAL CONDITIONING; ILLUMINATION EFFECTS; INFRARED (IR) SENSORS; INTERNATIONAL SOCIETY; LOW CONTRAST; LOW COST SENSORS; LOW COSTS; NUMERICA L RESULTS; OF SENSORS; OPTICAL (PET) (OPET); ORIENTATION ANGLES; POTENTIAL REGIONS; REAL IMAGES; RECOGNITION ALGORITHMS; RECOGNITION METHODS; RECOGNITION PERFORMANCE; REGION BASED APPROACH; SENSOR FUSION (SF); SURVEILLANCE APPLICATIONS; TARGET RECOGNITION;

AUTOMATIC TARGET RECOGNITION; BESSEL FUNCTIONS; COMPUTER NETWORKS; DETECTORS; FUSION REACTIONS; IMAGE CODING; IMAGE ENHANCEMENT; LEAD; MATHEMATICAL MODELS; MATRIX ALGEBRA; METROPOLITAN AREA NETWORKS; MULTITASKING; NETWORK PROTOCOLS; NUCLEAR PHYSICS; NUMERICAL METHODS; OPTICAL ENGINEERING; OPTICAL SENSORS; PHOTOACOUSTIC EFFECT; SENSOR DATA FUSION; SENSOR NETWORKS; SENSORS; SIGNAL PROCESSING; SULFATE MINERALS; TARGETS; TECHNOLOGY; TELECOMMUNICATION SYSTEMS;

DATA FUSION;

EID: 44949260151     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.777850     Document Type: Conference Paper

References (14)

1. Chen, X., Gong, S., Schmid, N. A., and Valenti, M., "UAV based distributed ATR under realistic simulated environmental effects," SPIE 6567, 65670E (2007).
2. Viola, P. and Jones, M., "Rapid object detection using a boosted cascade of simple features," IEEE CVPR (2001).
3. Schapire, R. and Singer, Y., "Improving boosting algorithms using confidence-rated predictions," (1999).
4. Srivastava, A., Liu, X., and Grenander, U., "Universal analytical forms for modeling image probabilities," IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 1200-1214 (Sept. 2002).
5. Srivastava, A., "Stochastic models for capturing image variability," IEEE Signal Processing Magazine 19, 63-76 (Sept. 2002).
6. Freund, J. and Perles, B., "A new look at quartiles of undergrouped data," American Stat. 41, 200-203 (1987).
7. Kullback, S., [Information Theory and Statistics], Dover Publications, New York (1997).
8. Figueiredo, M., Leitao, J., and Jain, A., "Unsupervised contour representation and estimation using b-splines and a minimum description length criterion," IEEE Trans. on Image Processing 9, 1171-1182 (May 2000).
9. Dierchx, P., [Curve and Surface Fitting with Splines], Oxford Univ. Press, Oxford, U.K. (1993).
10. Zhu, S. and Yuille, A., "Region competition: Unifying snakes, region growing, and bayes/mdl for multi-band image segmentation," IEEE Trans. On Pattern Analysis and Machine Intelligence 18(9), 884-900 (1996).
11. Grenander, U. and Srivastava, A., "Probability models for clutter in natural images," IEEE Transactions on Pattern Analysis and Machine Intelligence 23(4), 424-429 (2001).
12. http://www1.cs.columbia.edu/CAVE/software/softlib/coil 100.php
13. http://www cvr.ai.uiuc.edu/ponce.grp/data/
14. http://www.cs.ubc.ca/murphyk/Vision/objectRecognitionDatabases.html