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Vector ordering and multispectral morphological image processing

Abstract : 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 no-tions 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, al-lowing 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.
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Contributor : Santiago Velasco-Forero Connect in order to contact the contributor
Submitted on : Tuesday, January 27, 2015 - 4:34:14 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:12 PM
Long-term archiving on: : Tuesday, April 28, 2015 - 11:11:03 AM


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Santiago Velasco-Forero, Jesus Angulo. Vector ordering and multispectral morphological image processing. Vector Ordering and Multispectral Morphological Image Processing, 11, pp.223-239, 2014, Advances in Low-Level Color Image Processing, ⟨10.1007/978-94-007-7584-8_7⟩. ⟨hal-01110207⟩



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