SVM approximation for real-time image segmentation by using an improved hyperrectangles-based method

Real-Time Imaging

Volumn 9, Issue 3, 2003, Pages 179-188

  Miteran J ^a   Bouillant, S ^a   Bourennane, E ^a  

^a UNIVERSITÉ DE BOURGOGNE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; LEARNING ALGORITHMS; PATTERN RECOGNITION; REAL TIME SYSTEMS;

HYPERRECTANGLES BASED METHOD; REAL TIME SYSTEM IMAGE SEGMENTATION; SUPPORT VECTOR MACHINES;

IMAGE SEGMENTATION;

EID: 0142123423     PISSN: 10772014     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1077-2014(03)00035-4     Document Type: Article

References (21)

1. Duda RO, Hart PE. Pattern classification and scene analysis. New york: Wiley; 1973. p. 230-43.
2. Bishop CM. Neural networks for pattern recognition. Oxford: Oxford University Press: 1995. pp. 110-230.
3. Vapnik V. The nature of statistical learning theory. New York: Springer-Verlag; 1995.
4. Niyogi P, Burges C, Ramesh P. Distinctive feature detection using support vector machines. ICASSP'99, Phoenix, Arizona, USA, vol. 1; 1999. p. 425-28.
5. Schölkopf B, Smola A, Müller K-R, Burges CJC, Vapnik V. Support Vector methods in learning and feature extraction. Australian Journal of Intelligent Information Processing Systems 1998;1:3-9.
6. Jonsson K, Kittler J, Li YP, Matas J. Support vector machines for face authentication. In: Pridmore T, Elliman D, editors. British Machine Vision Conference, London, UK; 1999. p. 543-53.
7. Mitéran J, Gorria P, Robert M. Classification géométrique par polytopes de contraintes. Performances et intégration. Traitement du Signal 199;11:393-408.
8. Wettschereck D, Dietterich T. An experimental comparison of the nearest-neighbor and nearest-hyperrectangle algorithms. Machine Learning 1995;19(1):5-27.
9. Salzberg S. A nearest hyperrectangle learning method. Machine Learning. 1991;6:251-76.
10. Robert M, Gorria P, Mitéran J, Turgis S. Architectures for real-time classification processor. Custom Integrated Circuit Conference, San Diego, CA; 1994. p. 197-200.
11. Mitéran J, Zimmer JP, Yang F, Paindavoine M. Access control: adaptation and real-time implantation of a face recognition method. Optical Engineering 2001;40(4):586-93.
12. Dubuisson B. Diagnostic et reconnaissance des formes. Paris: Hermes; 1990.
13. Mitéran J, Geveaux P, Bailly R, Gorria P. Real-time defect detection using image segmentation. Proceedings of IEEE-ISIE'97, Guimares, Portugal; 1997. p. 713-16.
14. Enzler R, Jeger T, Cottet D, Tröster G. High-level area and performance estimation of hardware building blocks on FPGAs. In: Reiner Hartenstein, Grunbacher H, editors. Field-programmable logic and applications (Proceedings FPL'00), Lecture notes in computer science, vol. 1896, Berlin: Springer; 2000. p. 525-34.
15. Hauck S. The roles of FPGAs in reprogrammable systems. Proceedings of the IEEE 1998;86(4):615-38.
16. Hearst MA, Schölkopf B, Dumais S, Osuna E, Platt J. Trends and controversies - support vector machines. IEEE Intelligent Systems 1998;13(4):18-28.
17. Chapman K. Constant coefficient multipliers for the XC4000E, Xilinx application, Note XAPP054. Xilinx, Inc.; 1996.
18. Kittler J, Hatef M, Duin RPW, Matas J. On combining classifiers in. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998;20(3):226-39.
19. Moobed B. Combinaison de classifieurs, une nouvelle approche, PhD thesis, Laboratoire d'informatique de polytechnique d'Orsay; France; 1996.
20. Kittler J. Feature set search algorithms. In: Chen CH, editor. Pattern recognition and signal processing. Alphen aan den Rijn, Netherlands: Sijthoff and Noordhoff; 1978. p. 41-60.
21. Somol P, Pudil P, Novovocova J, Paclik P. Adaptative floating search methods in feature selection. Pattern Recognition Letters 1999;20:157-63.