Mathematical Morphology and Its Application to Signal and Image Processing
9th International Symposium on Mathematical Morphology, ISMM 2009 Groningen, The Netherlands, August 24-27, 2009 Proceedings
Herausgegeben: Wilkinson, Michael H. F.; Roerdink, Jos B.T.M.
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The 9th ISMM conference covered a very diverse collection of papers, bound together by the central themes of mathematical morphology, namely, the tre- ment of images in terms of set and lattice theory. Notwithstanding this central theme, this ISMM showed increasing interaction with other ?elds of image and signal processing, and several hybrid methods were presented, which combine the strengths of traditional morphological methods with those of, for example, linear ?ltering.This trendis particularlystrong in the emerging?eld of adaptive morphological ?ltering, where the local shape of structur...
The 9th ISMM conference covered a very diverse collection of papers, bound together by the central themes of mathematical morphology, namely, the tre- ment of images in terms of set and lattice theory. Notwithstanding this central theme, this ISMM showed increasing interaction with other ?elds of image and signal processing, and several hybrid methods were presented, which combine the strengths of traditional morphological methods with those of, for example, linear ?ltering.This trendis particularlystrong in the emerging?eld of adaptive morphological ?ltering, where the local shape of structuring elements is det- mined by non-morphological techniques. This builds on previous developments of PDE-based methods in morphology and amoebas. In segmentation we see similar advancements, in the development of morphological active contours. Even within morphology itself, diversi?cation is great, and many new areas of research are being opened up. In particular, morphology of graph-based and complex-based image representations are being explored. Likewise, in the we- established area of connected ?ltering we ?nd new theory and new algorithms, but also expansion into the direction of hyperconnected ?lters. New advances in morphological machine learning, multi-valued and fuzzy morphology are also presented. Notwithstanding the often highly theoretical reputation of mathematical morphology, practitioners in this ?eld have always had an eye for the practical.
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