Hierarchical rule-based neural network for multi-object classification using invariant features

INISTA 2011 - 2011 International Symposium on INnovations in Intelligent SysTems and Applications

Volumn , Issue , 2011, Pages 296-299

  Imamoglu N ^a   Eresen, A ^b   Ozbayoglu A M ^c  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

^c TOBB UNIVERSITY OF ECONOMICS AND TECHNOLOGY   (Turkey)

Author keywords

hierarchical rule based neural networks; nearest neighbor and Bayesian classifiers; pseudo zernike moments

Indexed keywords

BAYESIAN CLASSIFIER; FEATURE EXTRACTION TECHNIQUES; FEATURE EXTRACTOR; HIERARCHICAL CLASSIFICATION; HIERARCHICAL RULE-BASED NEURAL NETWORKS; INVARIANT FEATURES; MULTI-CLASS; MULTIOBJECT; NEAREST NEIGHBORS; OBJECT CLASSIFICATION; PSEUDO-ZERNIKE MOMENTS; ROTATION INVARIANT; RULE BASED; SIMILARITY MEASURE;

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; HIERARCHICAL SYSTEMS; INTELLIGENT SYSTEMS; PATTERN RECOGNITION SYSTEMS;

NEURAL NETWORKS;

EID: 79961198605     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/INISTA.2011.5946079     Document Type: Conference Paper

References (11)

1. R. J. Prokorp and A. P. Reeves, "A survey of moment-based techniques for unoccluded object representation and recognition", CVGIP: Graphical Models and Image Processing, vol. 54, pp. 438-460, 1992.
2. T. W. Lin and Y. F. Chou, "A comparative study of zernike moments", Proc. IEEE/WIC Int. Conf. on Web Intelligence, pp. 516-519, 2003.
3. S. Russel, P. Novig, R. O. Duda, P. E. Hart, and D.G. Stork, Artificial Intelligence: A Modern Approach, Prentice Hall. Upper Saddle River, New Jersey, 2003.
4. S. Theodoridis and K. Koutroumbas, Pattern Recognition. Academic Press, Elsevier, USA, 2006.
5. C. H. Teh and R. T. Chin, "On image analysis by the methods of moments", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 10, no. 4, pp. 496-513, 1988.
6. A. Khotanzad and Y.H. Hong, "Invariant image recognition by zernike moments", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 12:489-497, 1990. (Pubitemid 20702892)
7. C. W. Chong, R. Mukundan, and P. Raveendran, "An efficient algorithm for fast computation of pseudo-zernike moments", Int. Journal of Pattern Recognition and Artificial Intelligence, vol. 17, no. 6, pp. 1011-1023, 2003.
8. K. L. Priddy and P. E. Keller, Artificial Neural Networks: An Introduction. SPIE- International Society for Optical Engineering, Bellingham, Washington, USA, 2005.
9. th Annual Int. Conf. on Artificial Neural Networks in Engineering, pp. 447-454, 2009.
10. M. I. A. Lourakis and A. A. Argyros, "Is Levenberg-Marquardt the most efficient optimization algorithm for implementing bundle adjustment?", Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1526-1531, 2005.
11. J. M. Geusebroek, G.J. Burghouts, and A.W.M. Smeulders, "The Amsterdam library of object images", Int. Journal Computer Vision, vol. 61, no. 1, pp. 103-112, 2005.