Titlebook: Mathematical Morphology and Its Applications to Image and Signal Processing; 10th International S Pierre Soille,Martino Pesaresi,Georgios K

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Pattern Recognition Using Morphological Class Distribution Functions and Classification Treesnto several spatial classes, which is performed using morphological image processing. By performing this classification with structuring elements of increasing sizes, the spatial class distribution functions are produced. These functions are normalized and sampled in order to obtain feature vectors

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Object Descriptors Based on a List of Rectangles: Method and Algorithmhms are often restricted to 1D structuring elements, combinations of 1D elements, or isotropic structuring elements (like circles), because of the lack of methods directly applicable to arbitrary shaped 2D structuring elements. While these descriptors have proved useful in the past, we propose an al

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Geodesic Attributes Thinnings and Thickenings (CC) which do not fulfil a given criterion. When the increasingness property is dropped, more general algebraic thinnings are obtained. We propose in this paper, to use criteria based on the geodesic diameter to build algebraic thinnings for greyscale images. An application to the extraction of cra

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Morphological Bilateral Filtering and Spatially-Variant Adaptive Structuring Functionsto generalize these linear approaches in order to introduce adaptive nonlinear filters which asymptotically correspond to spatially-variant morphological dilation and erosion. In particular, starting from the bilateral filtering framework and using the notion counter-harmonic mean, our goal is to pr

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