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Volume: 4 | Article ID: art00101
Classification-driven stochastic watershed. Application to multispectral segmentation
  DOI :  10.2352/CGIV.2008.4.1.art00101  Published OnlineJanuary 2008

The aim of this paper is to present a general methodology based on multispectral mathematical morphology in order to segment multispectral images. The methods consists in computing a probability density function pdf of contours conditioned by a spectral classification. The pdf is conditioned through regionalized random balls markers thanks to a new algorithm. Therefore the pdf contains spatial and spectral information. Finally, the pdf is segmented by a watershed with seeds (i.e. markers) coming from the classification.Consequently, a complete method, based on a classification-driven stochastic watershed is introduced. This approach requires a unique and robust parameter: the number of classes which is the same for similar images.Moreover, an efficient way to select factor axes, of Factor Correspondence Analysis (FCA), based on signal to noise ratio on factor pixels is presented.

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Guillaume Noyel, Jesús Angulo, Dominique Jeulin, "Classification-driven stochastic watershed. Application to multispectral segmentationin Proc. IS&T CGIV 2008/MCS'08 4th European Conf. on Colour in Graphics, Imaging, and Vision 10th Int'l Symp. on Multispectral Colour Science,  2008,  pp 471 - 476,

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