Titlebook: Mathematical Morphology and Its Applications to Signal and Image Processing; 13th International S Jesús Angulo,Santiago Velasco-Forero,Fern

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0302-9743 posium on Mathematical Morphology, ISMM 2017, held in Fontainebleau, France, in May 2017. .The 36 revised full papers presented together with 4 short papers were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections on algebraic theory, max-plus and max-mi

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Bandeirantes: A Graph-Based Approach for Curve Tracing and Boundary Trackingeoretical properties and discuss its relations with other popular methods, such as riverbed, live wire and G-wire. We compare the methods in a curve tracing task of an automatic grading system. The results show that the new method can significantly increase the system accuracy.

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Prior-Based Hierarchical Segmentation Highlighting Structures of Interesthod that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.

incubus

Conference proceedings 2017ased segmentation; trees and hierarchies; topological and graph-based clustering, classification and filtering; connected operators and attribute filters; PDE-based morphology; scale-space representations and nonlinear decompositions; computational morphology; object detection; and biomedical, material science and physical applications..

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0302-9743 stering, classification and filtering; connected operators and attribute filters; PDE-based morphology; scale-space representations and nonlinear decompositions; computational morphology; object detection; and biomedical, material science and physical applications..978-3-319-57239-0978-3-319-57240-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Morphological Perceptrons: Geometry and Training Algorithmseometry, which enables us to obtain a precise bound on the number of linear regions of the maxout unit, a popular choice for deep neural networks introduced recently. Finally, we present some relevant numerical results.