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Performance of a New Method of Multicomponent Images Segmentation in the Presence of Noise

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DOI: 10.4236/eng.2011.311134    3,931 Downloads   6,376 Views  

ABSTRACT

Any undesirable signal limiting to a degree or another the integrity and the intelligibility of a useful signal can be considered as noise. In the general rule, the good performance of a system is assured only if the level of power of the useful signal exceeds by several orders of magnitude that of the noise (signal to noise of a several tens of decibels). However certain elaborate methods of treatment allow working with very low signal to noise ratio in an optimal way any a priori knowledge available on the signal useful to interpret. In this work, we evaluate the robustness of the noise on a new method of multicomponent image segmentation developed recently. Two types of additional noises are considered, which are the Gaussian noise and the uniform noise, with varying correlation between the different components (or planes) of the image. Quantitative results show the influence of the noise level on the segmentation method.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

S. Ouattara, O. Asseu, A. Clément and B. Vigouroux, "Performance of a New Method of Multicomponent Images Segmentation in the Presence of Noise," Engineering, Vol. 3 No. 11, 2011, pp. 1082-1089. doi: 10.4236/eng.2011.311134.

References

[1] M. Ciuc, “Multicomponent Images Processing: Applica- tion to Color Imaging and Radar,” Ph.D. Thesis, Univer- sity of Bucarest, Roumanie, 2002.
[2] P. Lambert and L. Macaire, “Filtering and Segmentation: the Specificity of Colour Images,” Proceedings of the First International Conference on Color in Graphics and Image Processing (CGIP), Saint-Etienne, France, 2000, pp. 57-71.
[3] S. Ouattara, G. L. Loum and A. Clément, “Unsuper- vised Segmentation Method of Multicomponent Images Based on Fuzzy Connectivity Analysis in Multidimen- sional Histograms,” Engineering, Vol. 3, No. 3, 2011, pp. 203-214. doi:10.4236/eng.2011.33024
[4] A. Clément and B. Vigouroux, “A Compact Histogram for the Analysis of Multicomponent Images,” Proceedings of 18th conference GRETSI on Signal Processing, France, Vol. 1, 2001, pp. 305-307.
[5] J. Chanussot, A. Clément, B. Vigouroux and J. Chabod, “Lossless Compact Histogram Representation for Multi- Component Images: Application to Histogram Equalization,” Geoscience and Remote Sensing Symposim, (IGARSS), Proceedings of IEEE International, Vol. 6, 2003, pp. 3940- 3942. doi:10.1109/IGARSS.2003.1295321
[6] S. Ouattara and A. Clement, “Labelling of Compact Mul- tidimensional Histograms for Analysis of Multicompo- nent Images,” Proceedings of the 21st Conference GRETSI on the Image Processing, Troyes, 2007, pp. 85-88.
[7] P. Schmid, “Segmentation of Digitized Dermatoscopic Images by Two-Dimensional Color Clustering,” IEEE Transaction on Medical Imaging, Vol. 18, No. 2, 1999, pp. 164-171. doi:10.1109/42.759124
[8] F. Kurugollu, B. Sankur and A. C. Harmanci, “Color Image Segmentation Using 2-Diomensional Histogram Multitresholding and Fusion,” Proceedings of the First International Conference on Color Graphics and Image Processing, Saint-Etienne, France, 2000, pp. 152-157. doi:10.1016/S0262-8856(01)00052-X
[9] A. Clément and B. Vigouroux, “Unsupervised Classifica- tion of Pixels in Color Images by Hierarchical Analysis of Bi-Dimensional Histograms,” IEEE International Conference on Systems Man and Cybernetics, Hamma- met, Tunisia, Vol. 2, 2002, pp. 85-89.
[10] O. Lezoray, “Unsupervised 2D Multiband Histogram Clustering and Region for Color Image Segmentation,” Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, 2003, pp. 267-270.
[11] O. Lezoray and C. Charrier, “Color Image Segmentation Using Morphological Clustering and Fusion with Auto- matic Scale Selection,” Pattern Recognition Letters, Vol. 30, No. 4, 2009, pp. 397-406. doi:10.1016/j.patrec.2008.11.005
[12] R. Furferi, “Colour Classification Method for Recycled Melange Fabrics,” Journal of Applied Science, Vol. 1, No. 1, 2011, pp. 236-246. doi:10.3923/jas.2011.236.246
[13] S. Yamada and K. Murase, “Effectiveness of Flexible Noise Control Image Processing for Digital Portal Images Using Computed Radiography,” British Journal of Radiology, Vol. 78, 2005, pp. 519-527. doi:10.1259/bjr/26039330
[14] S. H. Lim, “Characterization of Noise in Digital Photo- graphs for Image Processing,” Proceeding in Digital Photography II, IS&T/SPIE Electronic Imaging, Vol. 6069, 2008. doi:10.1117/12.655915
[15] J-P. Coquerez and S. Philipp, “Image Analysis: Filtering and Segmentation,” Masson, Paris, 1995.
[16] L. Vinet, “Segmentation and Mapping of Areas of Stereo- scopic Pairs of Images,” Ph.D. Thesis, University of Paris IX Dauphine, Paris, 1991.
[17] M. Borsotti, P. Campadelli and R. Schettini, “Quantita- tive Evaluation of Color Image Segmentation Results,” Pattern Recognition Letters Vol. 19, No. 8, 1998, pp. 741-747. doi:10.1016/S0167-8655(98)00052-X
[18] C. Rosenberger, “Adaptative Evaluation of Image Seg- mentation Results,” Proceeding of 18th International Conference on Pattern Recognition, Vol. 2, 2006, pp. 399-402. doi:10.1109/ICPR.2006.214
[19] H. Zhang, J. E. Fritts and S. A. Goldman, “Image Segmentation Evaluation: A Survey of Unsupervised Methods,” Computer Vision and Image Understanding, Vol. 110, No. 2, 2008, pp. 260-280. doi:10.1016/j.cviu.2007.08.003

  
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