Adjustable invariant features by partial Haar-integration

Proceedings - International Conference on Pattern Recognition

Volumn 2, Issue , 2004, Pages 769-774

  Haasdonk, Bernard ^a   Halawani, Alaa ^a   Burkhardt, Hans ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

OBJECT REPRESENTATION; PARTIAL HAAR-INTEGRATION; TRANSFORMATION GROUPS; VECTOR-VALUED REPRESENTATION;

CHARACTER RECOGNITION; ERROR ANALYSIS; IMAGE RETRIEVAL; INTEGRAL EQUATIONS; INTEGRATION; MATHEMATICAL TRANSFORMATIONS; MONTE CARLO METHODS; OPTIMIZATION; REGRESSION ANALYSIS; THEOREM PROVING; VECTORS;

FEATURE EXTRACTION;

EID: 10044269547     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2004.1334372     Document Type: Conference Paper

References (13)

1. O. Al-Jarrah and A. Halawani. Recognition of gestures in arabic sign language using neuro-fuzzy systems. Artificial Intelligence, 133(1-2): 117-138, December 2001.
2. T. Binford and T. Levitt. Quasi-invariants: Theory and exploitation. In O. Firschein, editor, DARPA Image Understanding Workshop Proceedings, pages 819-830, Washington DC, 1993. Morgan Kauffman.
3. H. Burkhardt and S. Siggelkow. Invariant features in pattern recognition - fundamentals and applications. In Non-linear Model-Based Image/Video Processing and Analysis, pages 269-307. John Wiley & Sons, 2001.
4. C.-C. Chang and C.-J. Lin. LIBSVM - a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
5. A. Kadyrov and M. Petrou. The trace transform and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8):811-828, August 2001.
6. D. Keysers, J. Dahmen, T. Theiner, and H. Ney. Experiments with an extended tangent distance. In Proceedings 15th International Conference on Pattern Recognition, vol. 2, pages 38-42. IEEE Computer Society, 2000.
7. O. Ronneberger, E. Schultz, and H. Burkhardt. Automated pollen recognition using 3d volume images from fluorescence microscopy. Aerobiologia, (18): 107-115, 2002.
8. M. Schael. Texture defect detection using invariant textural features. In B. Radig and S. Florczyk, editors, Proc. of the 23rd DAGM - Symposium Mustererkennung, LNCS, 2191, pages 17-24. Springer Verlag, 2001.
9. B. Schölkopf, C. Burges, and V. Vapnik. Incorporating invariances in support vector learning machines. In Artificial Neural Networks - ICANN'96, LNCS, 1112, pages 47-52. Springer, 1996.
10. H. Schulz-Mirbach. Constructing invariant features by averaging techniques. In Proceedings 12th International Conference on Pattern Recognition, vol. 2, pages 387-390. IEEE Computer Society, 1994.
11. S. Siggelkow. Feature-Histograms for Content-Based Image Retrieval. PhD thesis, Albert-Ludwigs-Universität Freiburg, 2002.
12. P. Y. Simard, Y. A. LeCun, and J. S. Denker. Efficient pattern recognition using a new transformation distance. In Advances in Neural Information Processing Systems, 5, pages 50-58. Morgan Kaufmann, San Mateo, CA, 1993.
13. T. Suk and J. Flusser. Combined blur and affine moment invariants and their use in pattern recognition. Pattern Recognition, 36(12):2895-2907, 2003.