Heterogeneous stacking for classification-driven watershed segmentation

Eurasip Journal on Advances in Signal Processing

Volumn 2008, Issue , 2008, Pages

  Levner, Ilya ^a   Zhang, Hong ^a   Greiner, Russell ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FEATURE EXTRACTION; IMAGE CLASSIFICATION; PIXELS; PROBABILITY;

CLASSIFICATION-DRIVEN WATERSHED SEGMENTATION (CDWS); STACKED GENERALIZATION; WATERSHED SEGMENTATION;

IMAGE SEGMENTATION;

EID: 43949096946     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1155/2008/485821     Document Type: Article

References (32)

1. Beucher S., Meyer F., Dougherty E., The morphological approach to segmentation: the watershed transformation Mathematical Morphology in Image Processing 1992 New York, NY, USA Marcel Dekker
2. Gonzalez R. C., Woods R. E., Digital Image Processing 2002 2nd Upper Saddle River, NJ, USA Prentice Hall
3. Bleau A.
4. Adams R., Bischof L., Seeded region growing IEEE Transactions on Pattern Analysis and Machine Intelligence 1994 16 6 641 647
5. Fan J.
6. Lezoray O.
7. Lezoray O., Cardot H., Cooperation of color pixel classification schemes and color watershed: a study for microscopic images IEEE Transactions on Image Processing 2002 11 7 783 789
8. Levner I.
9. Wolpert D. H., Stacked generalisation Neural Networks 1992 5 2 241 259
10. Hastie T., Tibshirani R., Friedman J., The Elements of Statistical Learning 2001 New York, NY, USA Springer Springer Series in Statistics
11. Webb A., Statistical Pattern Recognition 2002 New York, NY, USA John Wiley Sons
12. Breiman L.
13. Jacobs R. A., Jordan M. I., Nowlan S. J., Hinton G. E., Adaptive mixtures of local experts Neural Computing 1991 3 79 87
14. Freund Y., Schapire R. E., A decision-theoretical generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 1997 55 1 119 139
15. Raviv Y.
16. Breiman L.
17. Martínez-Muoz G.
18. Ting K. M.
19. Haralick R. M., Statistical and structural approaches to texture Proceedings of the IEEE 1979 67 5 786 804
20. Ohanian P. P., Dubes R. C., Performance evaluation for four classes of textural features Pattern Recognition 1992 25 8 819 833
21. Randen T.
22. Haralick R. M., Shanmugam K., Dinstein I., Textural features for image classification IEEE Transactions on Systems, Man, and Cybernetics 1973 3 6 610 621
23. Ojala T., Pietikinen M., Unsupervised texture segmentation using feature distributions Pattern Recognition 1999 32 3 477 486
24. Cohen F. S., Fan Z., Patel M. A., Classification of rotated and scaled textured images using Gaussian Markov random field models IEEE Transactions on Pattern Analysis and Machine Intelligence 1991 13 2 192 202
25. De Bonet J. S., Viola P. A., Jordan M. I., Kearns M. J., Solla S. A., A nonparametric multi-scale statistical model for natural images Advances in Neural Information Processing Systems 1998 10 Cambridge, Mass, USA MIT Press
26. Tieu K., Viola P. A., Boosting image retrieval 1 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '00) June 2000 Hilton Head Island, SC, USA 228 235
27. Hoyer P. O.
28. Hyvrinen A., Karhunen J., Oja E., Independent Component Analysis 2001 New York, NY, USA Wiley-Interscience
29. Hyvrinen A., Oja E., Independent component analysis: algorithms and applications Neural Networks 2000 13 4-5 411 430
30. Hyvrinen A., Fast and robust fixed-point algorithms for independent component analysis IEEE Transactions on Neural Networks 1999 10 3 626 634
31. Paclík P.
32. Duin R. P. W., Juszczak P., Paclík P., Pekalska E., de Ridder D., Tax D. M. J., PRTools4, A Matlab Toolbox for Pattern Recognition 2004. Delft University of Technology