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Classification-Driven Stochastic Watershed: Application to Multispectral Segmentation

Abstract : 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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Submitted on : Monday, March 13, 2017 - 2:39:02 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:13 PM
Long-term archiving on: : Wednesday, June 14, 2017 - 1:41:42 PM


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  • HAL Id : hal-00830667, version 1


Guillaume Noyel, Jesus Angulo, Dominique Jeulin. Classification-Driven Stochastic Watershed: Application to Multispectral Segmentation. 4th European Conference on Colour in Graphics, Imaging, and Vision and 10th International Symposium on Multispectral Colour Science (CGIV 2008/MCS'08), Jun 2008, Terrassa - Barcelona, Spain. pp.471-476. ⟨hal-00830667⟩



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