Ball detection in static images with Support Vector Machines for classification

Image and Vision Computing

Volumn 21, Issue 8, 2003, Pages 675-692

  Ancona, N ^a   Cicirelli, G ^a   Stella, E ^a   Distante, A ^a  

^a INSTITUTE OF INTELLIGENT SYSTEMS FOR AUTOMATION   (Italy)

Author keywords

Classification; Object detection; Support Vector Machines

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER VISION; ERROR ANALYSIS; LENSES; PARAMETER ESTIMATION; PROBLEM SOLVING; VIDEO CAMERAS;

STATIC IMAGES;

IMAGE PROCESSING;

EID: 0037715349     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0262-8856(03)00063-5     Document Type: Article

References (18)

1. I. Reid, A. Zisserman, Goal-directed video metrology, in: Fourth European Conference on Computer Vision'96, vol. B, Cambridge, UK, 1996, pp. 647-658.
2. Brunelli R., Poggio T. Face recognition: features versus templates. IEEE Transactions on Pattern Analysis and Machine Intelligence. 15:(10):1993;1042-1052.
3. Sung K., Poggio T. Example-based learning for view-based human face detection. 1994;Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA. A.I. Memo No. 1521.
4. Poggio T., Girosi A. A theory of networks for approximation and learning. 1989;Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA. A.I. Memo No. 1140.
5. Girosi F., Jones M., Poggio T. Regularization theory and neural networks architectures. Neural Computation. 7:1995;219-269.
6. Vapnik V. The Nature of Statistical Learning Theory. 1995;Springer, Berlin.
7. Vapnik V. An overview of statistical learning theory. IEEE Transactions on Neural Networks. 10:(5):1999;988-999.
8. Evgenious T., Pontil M., Poggio T. A unified framework for regularization networks and support vector machines. 1999;Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA. A.I. Memo No. 1654.
9. Courant R., Hilbert D. Methods of Mathematical Physics. vol. 1:1989;Wiley, New York.
10. Jain A.K. Fundamentals of Digital Image Processing. 1989;Prentice Hall, Englewood Cliffs, NJ.
11. Scholkopf B., Sung K., Burges C., Girosi F., Niyogi P., Poggio T., Vapnik V. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. 1996;Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA. A.I. Memo No. 1599.
12. Pontil M., Verri A. Properties of support vector machines. 1997;Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA. A.I. Memo No. 1612.
13. Blum A., Langley P. Selection of relevant features and examples. Machine Learning, Artificial Intelligence. 97:1997;245-271.
14. Weston J., Mukherjee S., Chapelle O., Pontil M., Poggio T., Vapnik V. Feature selection for SVMs. Advances in Neural Information Processing Systems. 13:2000;668-674.
15. Heisele B., Poggio T., Pontil M. Face detection in still gray images. 2000;Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA. A.I. Memo No. 1687.
16. G. Cicirelli, N. Ancona, G. Attolico, A. Distante, Object positioning by projective properties, in: Proceedings of the IASTED International Conference on Applied Simulation and Modeling, Marbella, Spain, 2001, pp. 52-57.
17. A. Haas, R. Maierhofer, R. Sendlhofer, Goalwatcher: a system for the automatic detection of the ball position in soccer games, in: Proceedings of SPIE Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II, vol. 4567, Boston, Massachusetts, 2001.
18. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition. 30:1997;1145-1159.