A generic moment invariants based supervised learning framework for classification using partial object information

Proceedings of the 2009 Canadian Conference on Computer and Robot Vision, CRV 2009

Volumn , Issue , 2009, Pages 45-52

  Minhas, Rashid ^a   Mohammed, Abdul Adeel ^a   Wu, Q M Jonathan ^a  

^a UNIVERSITY OF WINDSOR   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST ALGORITHM; ADAPTIVE SELECTION; CLASSIFICATION RATES; CLASSIFICATION SCHEME; DISTANCE TRANSFORMS; HU MOMENTS; MOMENT INVARIANT; NEIGHBORHOOD INFORMATION; OBJECT INFORMATION; OCCLUDED OBJECTS; PARTIAL INFORMATION; ROTATIONAL TRANSFORMATION; STATE-OF-THE-ART SCHEME;

ADAPTIVE BOOSTING; CLASSIFICATION (OF INFORMATION); COMPUTER VISION;

INFORMATION USE;

EID: 70350554269     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CRV.2009.37     Document Type: Conference Paper

References (21)

1. Y. Freud, R.E. Schapire, "A decision theoretic generalization of on-line learning and an application to boosting", Journal of Computer and system sciences 55,1997, 119-139.
2. L.G. Valiant, "A theory of learnable", Artificial intelligence and language processing, Vol 27, 1984, 134-142
3. Saad Ali, Mubarak Shah, "A supervised learning framework for generic object detection in images", International conference on Computer Vision, China, Oct.2005
4. H. Schneiderman, T. Kanade, "Object detection using the statistics of parts", International Journal of C.V., 2004, pp 151-177
5. E. Bart, E. Byvatove, S. Ullman, "View-invariant recognition using corresponding object fragments", ECCV, Prague, May 2004
6. J. Amores et. al., "Boosting contextual information in content-based image retrieval", ACM SIGMM, 2004
7. J. Shotton, A. Blake, R. Cipolla, "Contour based learning for object detection", ICCV, China, October 2005
8. A. Opelt, A. Pinz, A. Zisserman, "Incremental learning of object detectors using a visual shape alphabet", International Conference on Computer Vision and Pattern Recognition,2006
9. P. Viola, M. Jones, "Rapid object detection using a Boosted cascade of simple feature", CVPR, 2001
10. R. Lienhart, A. Kuranov, V. Pisarevisky, "Empirical analysis of detection cascades of Boosted classifiers for rapid object detection", MRL technical report 2002.
11. R. Fergus, P. Perona, A. Zisserman, "Object class recognition by unsupervised scale-invariant learning",International Conference on Computer Vision and Pattern Recognition, 2003
12. A. Opelt , M. Fussenegger, A. Pinz, P. Auer, "Weak hypothses and boosting for generic object detection and recognition", European Conference on Computer Vision,2004
13. Meijster A., Roerdink, J.B.T.M., Hesselink, W.H., "A general algorithm for computing distance transform in linear time", Mathematical morphology and its application to image and signal processing, Kluwer academic publishers, 2000,331-340.
14. Borgefors G., "Distance transforms in digital images",Computer Vision Graphics Image Processing, 1986, 344-371
15. Danielsson, P.E., Euclidean distance mapping,Computer Vision Graphics Image Processing, 1980, 227-248.
16. Leymarie F., Levine M., "Fast raster scan distance propagation on the discrete rectangular lattice", Computer Vision Graphics Image Processing: Image Understanding, 1992, 84-94.
17. Raphael Finkel and J.L. Bentley, "Quad Trees: A Data Structure for Retrieval on Composite Keys", Acta Informatica 4 (1): 1974, 1-9.
18. http://en.wikipedia.org/wiki/Quadtree.
19. M.K. Hu, "Visual pattern recognition by moment invariants", IRE trans on Information Theory, 1962, 179-187.
20. M. Mercimek, K. Gulez, and T.V. Mumcu, "Real object recognition using moment invariants", sadhana vol. 30, 2005, 765-775.
21. J. Flusser, "Moments invariants in image analysis", Proc. of World Academy of Science, Engg and Tech., 2006, 196-201.