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.

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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.