Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation

Pattern Recognition Letters

Volumn 31, Issue 15, 2010, Pages 2364-2374

  Derivaux, S ^a   Forestier, G ^a   Wemmert, C ^a   Lefvre, S ^a  

^a UNIVERSITÉ DE STRASBOURG   (France)

Author keywords

Fuzzy classification; Genetic algorithm; Supervised image segmentation; Watershed transform

Indexed keywords

ELEVATION MAPS; EVOLUTIONARY COMPUTATIONS; FUZZY CLASSIFICATION; HOMOGENEOUS REGIONS; IMAGE INTERPRETATION; LEARNING SAMPLES; MACHINE-LEARNING; PRIOR KNOWLEDGE; REGION-BASED; REMOTELY SENSED IMAGES; SEGMENTATION ALGORITHMS; SEGMENTATION EVALUATION; SEGMENTATION PROCESS; SUPERVISED CLASSIFICATION; SUPERVISED IMAGE SEGMENTATION; WATERSHED SEGMENTATION; WATERSHED TRANSFORM;

FUZZY SYSTEMS; GENETIC ALGORITHMS; IMAGE ANALYSIS; LANDFORMS; LEARNING SYSTEMS; PIXELS; REMOTE SENSING; WATERSHEDS;

IMAGE SEGMENTATION;

EID: 77956395027     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2010.07.007     Document Type: Article

References (31)

1. D.W. Aha, D.F. Kibler, and M.K. Albert Instance-based learning algorithms Machine Learn. 6 1991 37 66
2. E. Aptoula, and S. Lefvre A comparative study on multivariate mathematical morphology Pattern Recognition 40 11 2007 2914 2929
3. B. Bhanu, S. Lee, and S. Das Adaptive image segmentation using genetic and hybrid search methods IEEE Trans. Aerosp. Electron. Systems 31 4 1995 1268 1291
4. T. Blaschke Object based image analysis for remote sensing ISPRS J. Photogramm. Remote Sens. 65 1 2010 2 10
5. T. Blaschke, S. Lang, E. Lorup, J. Strobl, and P. Zeil Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications Environ. Inform. Plan. Polit. Public 2 2000 555 570
6. A.P. Carleer, O. Debeir, and E. Wolff Assessment of very high spatial resolution satellite image segmentations Photogramm. Eng. Remote Sens. 71 11 2005 1285 1294
7. Chen, Y., Huang, F., Tagare, H., Rao, M., Wilson, D., Geiser, E., 2003. Using prior shape and intensity profile in medical image segmentation. In: Proc. 9th IEEE Internat. Conf. on Computer Vision, pp. 1117-1124.
8. Derivaux, S., 2009. Construction et classification d'objets partir d'images de télédétection par une approche itérative guidée par des connaissances du domaine. Ph.D. Thesis, University of Strasbourg.
9. Feitosa, R.Q., Costa, G.A., Cazes, T.B., Feijó, B., 2006. A genetic approach for the automatic adaptation of segmentation parameters. In: Internat. Conf. on Object-based Image Analysis.
10. C.M. Fonseca, and P.J. Fleming An overview of evolutionary algorithms in multiobjective optimization Evolut. Comput. 1 3 1996 1 16
11. A. Gersho, and R.M. Gray Vector Quantization and Signal Compression 1992 Kluwer Academic Publishers.
12. D. Goldberg, and J. Holland Genetic algorithms and machine learning Machine Learn. 3 2 1988 95 99
13. V. Grau, A. Mewes, M. Alcaniz, R. Kikinis, and S. Warfield Improved watershed transform for medical image segmentation using prior information IEEE Trans. Med. Imag. 23 4 2004 447 458
14. S. Haker, G. Sapiro, and A. Tannenbaum Knowledge-based segmentation of SAR data with learned priors IEEE Trans. Image Process. 9 2 2000 299 301
15. G. Hamarneh, and X. Li Watershed segmentation using prior shape and appearance knowledge Image Vision Comput. 2007 10.1016/j.imavis.2006.10.009
16. K. Haris, S.N. Efstradiadis, N. Maglaveras, and A.K. Katsaggelos Hybrid image segmentation using watersheds and fast region merging IEEE Trans. Image Process. 7 12 1998 1684 1699
17. P. Jaccard The distribution of flora in the alpine zone New Phytol. 11 2 1912 37 50
18. L. Janssen, and M. Molenaar Terrain objects, their dynamics and their monitoring by the integration of GIS and remote sensing IEEE Trans. Geosci. Remote Sens. 33 1995 749 758
19. S. Lefvre Knowledge from markers in watershed segmentation IAPR Internat. Conf. on Computer Analysis of Image and Patterns (CAIP) Lecture Notes in Computer Sciences vol. 4673 2007 Springer Vienna 579 586
20. I. Levner, and H. Zhang Classification-driven watershed segmentation IEEE Trans. Image Process. 16 5 2007 1437 1445
21. Lezoray, O., Charrier, C., Cardot, H., Lefvre, S. (Eds.), 2008. Machine Learning in Image Processing. EURASIP J. Adv. Signal Process.
22. Martin, V., Maillot, N.M.T., 2006. A learning approach for adaptive image segmentation. In: IEEE Internat. Conf. on Computer Vision Systems, pp. 40-48.
23. F. Meyer, and S. Beucher Morphological segmentation J. Visual Commun. Image Represent. 1 1 1990 21 46
24. L. Najman, and M. Schmitt Geodesic saliency of watershed contours and hierarchical segmentation IEEE Trans. Pattern Anal. Machine Intell. 18 12 1996 1163 1173
25. G. Pignalberi, R. Cucchiara, L. Cinque, and S. Levialdi Tuning range image segmentation by genetic algorithm EURASIP J. Appl. Signal Process. 2003 8 2003 780 790
26. P. Soille Morphological Image Analysis second ed. 2003 Springer-Verlag
27. A. Song, and V. Ciesielski Fast texture segmentation using genetic programming IEEE Congress Evolut. Comput. 3 2003 2126 2133
28. L. Vincent, and P. Soille Watersheds in digital spaces: An efficient algorithm based on immersion simulations IEEE Pattern Anal. Machine Intell. 13 6 1991 583 598
29. Y.J. Zhang A survey on evaluation methods for image segmentation Pattern Recognition 29 8 1996 1335 1346
30. Zhang, Y.J., 2001. A review of recent evaluation methods for image segmentation. In: Internat. Symposium on Signal Processing and Its Applications, vol. 1, pp. 148-151.
31. Zhang, Y.J., Gerbrands, J.J., 1992. Segmentation evaluation using ultimate measurement accuracy. In: Proc. SPIE, Image Processing Algorithms and Techniques III, vol. 1657, pp. 449-460.