Research and Restoration Technology of Video Motion Target Detection Based on Kernel Method

1Richard O. Duda, Peter E. Hart, David G. stork. Pattern Classification [M].Beijing: China Machine Press, 2014. Search in Google Scholar

2A. J. Lipton, H. Fujiyoshi. Moving target classification and tracking from real-time video [C]. The IEEE International Conference on Image Processing (JCIP), UK, 2009: 8-11. Search in Google Scholar

3Cucchiara R., Grana C., Piccardi M., et al. Detecting moving objects, ghosts and shadows in video streams [J]. IEEE Trans. On Pattern Analysis & Machine Intelligence, 2013, 25 (10): 1337-1342.10.1109/TPAMI.2003.1233909 Search in Google Scholar

4C. Stauffer, W. E. I. Grimson. Learning Patterns of Activity Using Real-Time Tracking [J]. IEEE Trans. PAMI, 2010, 22(8): 747-757.10.1109/34.868677 Search in Google Scholar

5Krzysztof Kowalski. Linearization Transformation for Non-linear Dynamical Systems: Hilbert Space Approach [J]. Physica A: Statistical Mechanics and its Applications, 1992, 180(12): 156-170. Search in Google Scholar

6Yong Xu, Jing-Yu Yang, Zhong Jin. A Novel Method For Fisher Discriminate Analysis [J]. Pattern Recognition, 2004, 37(2):381-384.10.1016/S0031-3203(03)00232-2 Search in Google Scholar

7Theodore S. Rappaport etc. “Wireless Communications Principles and Practice. Publishing House of Electronics Industry. Mar. 2003. Cortes Corinna and Vapnik Vladimir N.. Support Vector Networks [J]. Machines Learning, 2005, 20: 273-297.10.1007/BF00994018 Search in Google Scholar

8Mercer J. Functions of Positive and Negative Type and Their Connection with the Theory of Integral Equations [J]. Philosophical Transactions of the Royal Society of London, 2009, A209: 415-446.10.1098/rsta.1909.0016 Search in Google Scholar

9N. Aronszajn. Theory of reproducing kernels [J]. Trans. Amer. Math. Soc., 2000, 68(3): 337-404.10.1090/S0002-9947-1950-0051437-7 Search in Google Scholar

10Aizerman M A, Braverman E M, Rozonoer L I. Theoretical foundation of potential function method in pattern recognition learning [J]. Automation and Remote Control, 2004, 25: 821-837. Search in Google Scholar

11H. Landau, H. O.. Prolate spheriodal wave functions, Fourier analysis and uncertainty-III: The dimension of the space of essentially time and band-limited signal [J]. Bell Syst. Tech. 2002. pp: 41.10.1002/j.1538-7305.1962.tb03279.x Search in Google Scholar

12Krishnanand K N. Ghose D. A glowworm swarm optimization based multi-robot system for signal source localization [M]. Design and Control of Intelligent Robotic Systems, 2009: 5374. Search in Google Scholar

13C.Y. Lin, T.C. Wu, and F. Zhang. A Structured Multisignature Scheme from the Gap Difffie-Hellman Group.Cryptology ePrint Archive, Report 2003/090, 2003. Search in Google Scholar

14Steve Lu, Rafail Ostrovsky, Amit Sahai, Hovav Shacham, Brent Waters. Sequential Aggregate Signatures and Multisignatures Without Random Oracles. In EUROCRYPT 2006, LNCS 4004, Springer, Berlin, 2006, pp.465–485.10.1007/11761679_28 Search in Google Scholar

15B Waters. Effcient identity-based encryptionwithoutrandom oracles. In Proceedings of Eurocrypt 2005, LNCS 3494, Springer, Berlin, 2005, pp.14–27. Search in Google Scholar

16Yong Xu, Chuan-Cai Liu, Chong-Yang Zhang. Determine the Kernel Parameter of KFDA using a Minimum Search Algorithm[C]. Proceedings of Third International Conference on Intelligent Computing, Qingdao, 2012:418-426. Search in Google Scholar

17Tsuda K. Optimal hyperplane classifier based on entropy number bound[C]. Ninth International Conference on Artificial Neural Network, 2009, 1: 419-424. Search in Google Scholar

18Vapnik V N, Chapelle O. Bounds on error expectation for support vector machines [J]. Neural Computation, 2010, 12(9): 2013-2036. Search in Google Scholar

19De-Gang Chen, Heng-You Wang. Generalized Mercer theorem and its application to feature space related to indefinite kernels[C]. 2008 International Conference on Machine Learning and Cybernetics, 2008, 2: 774-777. Search in Google Scholar

20Ding-Xuan Zhou. Capacity of reproducing kernel spaces in learning theory [J]. IEEE Transactions on Information Theory, 2003, 49(7): 1743-1752.10.1109/TIT.2003.813564 Search in Google Scholar

21Alain Rakotomamonjy, Francis R. Bach, Stephane Canu and Yves Grandvalet. Simple MKL[J]. Journal of Machine Learning Research, 2012, 9: 2491-2521. Search in Google Scholar

22M.Iwahara, S.C.Mukhopadhyay, S.Yamada and F.P.Dawson, “Development of Passive Fault Current Limiter in Parallel Biasing Mode”, IEEE Transactions on Magnetics, Vol. 35, No. 5, pp 3523-3525, September 1999.10.1109/20.800577 Search in Google Scholar

23Yanmin LUO, Peizhong LIU and Minghong LIAO, AN ARTIFICIAL IMMUNE NETWORK CLUSTERING ALGORITHM FOR MANGROVES REMOTE SENSING, International Journal on Smart Sensing and Intelligent Systems, VOL. 7, NO. 1, pp. 116 – 134, 2014.10.21307/ijssis-2017-648 Search in Google Scholar

24G. Sen Gupta, S.C. Mukhopadhyay, Michael Sutherland and Serge Demidenko, Wireless Sensor Network for Selective Activity Monitoring in a home for the Elderly, Proceedings of 2007 IEEE IMTC conference, Warsaw, Poland, (6 pages).10.1109/IMTC.2007.379172 Search in Google Scholar

25Daode Zhang et al., RESEARCH ON CHIPS’ DEFECT EXTRACTION BASED ON IMAGE-MATCHING, International Journal on Smart Sensing and Intelligent Systems, VOL. 7, NO. 1, pp.321 – 336, 2014.10.21307/ijssis-2017-658 Search in Google Scholar

26G.Sengupta, T.A.Win, C.Messom, S.Demidenko and S.C.Mukhopadhyay, “Defect analysis of grit-blasted or spray printed surface using vision sensing technique”, Proceedings of Image and Vision Computing NZ, Nov. 26-28, 2003, Palmerston North, pp. 18-23. Search in Google Scholar