In this paper, a new formulation of patch-based adaptive mathematical morphology is addressed. In contrast to classical approaches, the shape of structuring elements is not modified but adaptivity is directly integrated into the definition of a patch-based complete lattice. The manifold of patches is learned with a nonlinear bijective mapping, interpreted in the form of a learned rank transformation together with an ordering of vectors. This ordering of patches relies on three steps: dictionary learning, manifold learning and out of sample extension. The performance of the approach is illustrated with innovative examples of patch-based image processing, segmentation and texture classification.
CITATION STYLE
Lézoray, O. (2015). Patch-based mathematical morphology for image processing, segmentation and classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9386, pp. 46–57). Springer Verlag. https://doi.org/10.1007/978-3-319-25903-1_5
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