Vehicle recognition using curvelet transform and SVM

Proceedings - International Conference on Information Technology-New Generations, ITNG 2007

Volumn , Issue , 2007, Pages 516-521

  Kazemi, Farhad Mohamad ^a   Samadi, Saeed ^b   Poorreza, Hamid Reza ^c   Akbarzadeh T, Mohamad R ^c  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b Khorasan Science & Technology Park   (Iran)

^c FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA STRUCTURES; OBJECT RECOGNITION; PARAMETER ESTIMATION; WAVELET TRANSFORMS;

CURVELET COEFFICIENTS; STANDARD DEVIATION PREPROCESSING; VEHICLE RECOGNITION;

SUPPORT VECTOR MACHINES;

EID: 34548133385     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITNG.2007.205     Document Type: Conference Paper

References (16)

1. MP. Dubuisson-Jolly, S. Lakshmanan, and A. Jain. Vehicle segmentation and classification using deformable templates. IEEE Transactions Pattern Analysis and Machine Intelligence, 18(3):293-308, 1996.
2. J. Ferryman, A . Worral, G. Sulliva, and K. Baker.A generic deformable model for vehicle recognition. In British Machine Vision Conference, pages 127-136. British Machine Vision Association, 1995.
3. W. Wei, Q. Zhang, and M. Wang. A method of vehicle classification using models and neural networks. In IEEE Vehicular Technology Conference. IEEE, 2001.
4. E.J. Candes, DL. Donoho, "Curvelets - A surprisingly effective nonadaptive representation for objects with edges", Curve and Surface Fitting, Vanderbilt Univ.Press 1999.
5. T. Kato, Y. Ninomiya, and I. Masaki. Preceding vehicle recognition based on learning from sample images. IEEE Transactions on Intelligent Transportation Systems, 3(4):252-260, 2002.
6. A. Lai, G. Fung, and N Yung. Vehicle type classification from visual-based dimension estimation. In IEEE Intelligent Transportation Systems Conference, pages 201-206. IEEE, 2001.
7. N. Matthews, P. An, D. Charnley, and C. Harris. Vehicle detection and recognition in greyscale imagery. Control Engineering Practice, 4(4):472-479, 1996.
8. V.S.Petrovic and T.F.Cootes. Analysis of Features for Rigid Structure Vehicle Type Recognition.2003.
9. L. Dlagnekov, Video-based Car Surveillance: License Plate, Make, and Model Recognition,Thesis , Masters of Science in Computer Science University of California, San Diego, 2005.
10. E.J. Candes, DL. Donoho, "New Tight Frames of Curvelets and Optimal Representations of Objects with Smooth Singularities", Technical Report, Stanford University, 2002.
11. DL. Donoho, "De-noising by soft-thresholding", IEEE Transactions on Information Theory, 1995.
12. B.S. Kashin, V.N. Temlyakov, "On best m-term approximations and the entropy of sets in the space L1" Mathematical Notes 56, 1137-1157, 1994.
13. E.J. Candes, L. Demanet, DL. Donoho, L. Ying, "Fast Discrete Curvelet Transforms" Technical Report, Cal Tech, 2005.
14. C. J.C. Burges, "A Tutorial on Suport Vector Machines for Pattern Recognition," Knowledge Discovery Data Mining, vol. 2, no.2, pp. 121-167, 1998.
15. C. J. C. Burges and B. Schoclkopf, "Improving speed and accuracy of Support Vector Learning Machines:' Advances in Neural information processing System, vol. 9, pp. 375-381, 1997.
16. S. Theodoridis, K. Koutroumbas, Pattern Recognition, Academic Press, New York, 1999.