Automatic target recognition via sparse representations

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7696, Issue , 2010, Pages

  Estabridis, Katia ^a  

^a NAVAL AIR WARFARE CENTER   (United States)

Author keywords

Automatic target recognition; Compressed sensing; Overcomplete dictionary; Sparse representations

Indexed keywords

ATR ALGORITHM; COMPRESSED SENSING; EMERGING TECHNOLOGIES; IMAGE MODALITY; IMAGE-BASED; INPUT IMAGE; LINEAR COMBINATIONS; LOW-DIMENSIONAL SPACES; NEW THEORY; NIGHT VISION AND ELECTRONIC SENSORS DIRECTORATE; ORTHOGONAL MATCHING PURSUIT; OVERCOMPLETE DICTIONARIES; RECOGNITION RATES; SPARSE REPRESENTATION; SPARSE REPRESENTATIONS; SPARSE VECTORS; TARGET DETECTION AND RECOGNITION; TEST SAMPLES;

ALGORITHMS; AUTOMATIC TARGET RECOGNITION; OPTICAL CHARACTER RECOGNITION; OPTICAL DATA PROCESSING; SIGNAL RECONSTRUCTION; TARGETS;

PATTERN RECOGNITION SYSTEMS;

EID: 77953750401     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.849591     Document Type: Conference Paper

References (19)

1. Kumar, D; Varma, S., "Multiresolution Framework with Neural Network Approach for Automatic Target Recognition," IEEE ICSAP, (2009).
2. Ning, W., Chen, W., and Zhang, X., "Automatic target recognition of ISAR object images based on neural networks," IEEE Conf. Neural Networks and Sig. Proc, (2003).
3. Sun, S., Kwak, D., Jang, W., Kim, D., "Small target detection using center-surround difference with locally adaptive threshold," Proc. Intl. Symp. on Image and Sig. Processing and Analysis, (2005)
4. Arivazhagan, S., and Ganesan, L., "Automatic Target detection using Wavelet Transform", EURASIP Journal on Applied Signal Processing, (2004).
5. Stanford, D., Pitton, J., "Multiresolution Hidden Markov tress for analysis of automatic target recognition algorithms," IEEE, (2000)
6. Mahalanobis, A. Muise, R.R. Stanfill, S.R. Van Nevel, A., "Design and application of quadratic correlation filters for target detection," IEEE Trans. Aero. and Elec. Systems, (2004).
7. Candes, E., "Compresses Sampling," Proc. of the Int. Congress of Mathematics, (2006).
8. Candes, E., Romberg J., and Tao, T., "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inform. Theory, vol. 5, pp. 489-509, (2006).
9. Wright, J., Yang, A., Ganesh, A., Sastry, S., and Ma, Y., "Robust Face Recognition via Sparse Representation," IEEE Transactions, PAMI, Volume 31, Issue 2, (2009).
10. Guo, Z., and Osher, S., "Template matching via 11 minimization and its application to hyperspectral target detection,", UCLA, (2009).
11. Candes, E., and Tao, T., "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, (2005).
12. Gilbert, A., and Tropp, J., "Signal Recovery from random measurements via orthogonal matching pursuit", IEEE Trans. Inf. Theory, vol. 53, no. 12, (2007)
13. Rauhut, H., Schnass, K., and Vandergheynst, P., "Compressed Sensing and Redundant Dictionaries," IEEE Trans. Inf. Theory, vol. 54, no. 5, (2008).
14. 1 minimization," Proc. Nat. Aca. Sci., vol. 100, no. 5, (2003).
15. Gribonval, R. and Nielsen, M., "Sparse representations in unions of bases," IEEE Trans. Inf. Theory, vol. 49, no. 12, (2003).
16. Tropp, J., "Greed is good: algorithmic results for sparse approximation," IEEE Trans. Inf. Theory, vol. 50, no. 10, (2004).
17. Pati Y., Rezaiifar R., and Krishnaprasad, P., "Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition," Proc. 27th Annual Asilomar Conf. Signals, Systems, and Computers, vol. 1, (1993).
18. Tibshirani, R. "Regression shrinkage and selection via the lasso," J. Royal. Statist. Soc B., Vol. 58, No. 1, (1996).
19. Zhao, P., Yu, B., "On model selection consistency of lasso," Journal of Machine Learning Research, No. 7, (2006)