Vector Ordering and Multispectral Morphological Image Processing



This chapter illustrates the suitability of recent multivariate ordering approaches to morphological analysis of colour and multispectral images working on their vector representation. On the one hand, supervised ordering renders machine learning notions and image processing techniques, through a learning stage to provide a total ordering in the colour/multispectral vector space. On the other hand, anomaly-based ordering, automatically detects spectral diversity over a majority background, allowing an adaptive processing of salient parts of a colour/multispectral image. These two multivariate ordering paradigms allow the definition of morphological operators for multivariate images, from algebraic dilation and erosion to more advanced techniques as morphological simplification, decomposition and segmentation. A number of applications are reviewed and implementation issues are discussed in detail.


Multivariate mathematical morphology Supervised ordering  Segmentation Complete lattice Statistical depth function 


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© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.ITWM - Fraunhofer InstituteKaiserslauternGermany
  2. 2.Mathématiques et SystèmesCMM-Centre de Morphologie MathématiqueFontainebleau CEDEXFrance

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