Active learning for detection of mine-like objects in side-scan sonar imagery

IEEE Journal of Oceanic Engineering

Volumn 30, Issue 2, 2005, Pages 360-371

  Dura, Esther ^a   Zhang, Yan ^a   Liao, Xuejun ^a   Dobeck, Gerald J ^b   Carin, Lawrence ^a  

^a Duke University   (United States)

^b CSS   (United States)

Author keywords

Active learning; Classification; Detection; Mine like; Side scan sonar; Target; Unmanned underwater vehicle (UUV)

Indexed keywords

ADAPTIVE ALGORITHMS; AUTOMATIC TARGET RECOGNITION; LEARNING ALGORITHMS; OBJECT RECOGNITION; SONAR; UNDERWATER EQUIPMENT; UNDERWATER IMAGING; UNDERWATER PROBING; UNMANNED VEHICLES;

ACTIVE LEARNING; ACTIVE-LEARNING ALGORITHM; MINE-LIKE OBJECTS; SIDE-SCAN SONAR IMAGERY; UNMANNED UNDERWATER VEHICLE (UUV);

OCEAN ENGINEERING;

EID: 27844451490     PISSN: 03649059     EISSN: None     Source Type: Journal    
DOI: 10.1109/JOE.2005.850931     Document Type: Article

References (15)

1. G. J. Dobeck, "Automated detection/classification of sea mines in sonar imagery," in Detection and Remediation Technologies for Mines and Minelike Targets II, Proc. Int. Society Optical Engineering (SPIE), vol. 3079, Orlando, FL, 1997, pp. 913-110.
2. A. R. Castellano and B. C. Gray, "Autonomous interpretation of side scan sonar returns," in Proc. Symp. Autonomous Underwater Vehicle Technology, Washington, DC, 1990, pp. 248-253.
3. P. F. Schweizer and W. J. Petlevich, "Automatic target detection and cuing system for an autonomous underwater vehicle," in Proc. Int. Symp. Unmanned Untethered Submersible Technology, Elliott City, MD, 1989, pp. 359-371.
4. G. J. Dobeek, "Algorithm fusion for automated sea mine detection and classification," in Proc. IEEE OCEANS Conf., Honolulu, HI, 2001, pp. 5-8.
5. J. C, Delvigne, "Shadow classification using neural networks," in Proc. Undersea Defense Conf., London, U.K., 1992, pp. 214-221.
6. B. Schölkopf and A. Smola, Learning With Kernels, Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MA: MIT Press, 2002.
7. B. Schölkopf, K.-K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, "Comparing support vector maehines with Gaussian kernels to radial basis function classifiers," IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2758-2765, Nov. 1997.
8. M. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, no. 3, pp. 211-244, 2001.
9. V. V. Fedorov, Theory of Optimal Experiments. New York: Academic, 1972.
10. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Wiley, 1991.
11. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Boston, MA: Kluwer, 1991.
12. T. Kohonen, "Learning vector quantization," Neural Netw., vol. 1, no. 3, pp. 303-315, 1988.
13. I. T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
14. L. Costa and M. Cesar, Shape Analysis and Classification. Boca Raton, FL: CRC Press, 1998.
15. S. Theodoris and K. Koutroumbas, Pattern Recognition. San Diego, CA: Academic, 1999.