Titlebook: Discrete Geometry and Mathematical Morphology; Third International Sara Brunetti,Andrea Frosini,Simone Rinaldi Conference proceedings 2024

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New Characterizations of Full Convexity from dimension 3. If we consider now .-simplices instead of a segment (2-simplex), we achieve an equivalence in arbitrary dimension .. Secondly, we exhibit another characterization of full convexity, which is recursive with respect to the dimension and uses simple axis projections. This latter char

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Differential Maximum Euclidean Distance Transform Computation in Component Treesute twice as fast as the node-reconstruction approach. Our proposed attribute is increasing and its applicability is exemplified by the design of an extinction value filter. The ability to select thin connected components, like cables, of our filter is compared to filters using other increasing attr

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An Algorithm to Train Unrestricted Sequential Discrete Morphological Neural Networkse is composed only of operators that can be decomposed as the supremum, infimum, and complement of erosions and dilations. In this paper, we propose an algorithm to learn unrestricted sequential DMNN, whose architecture is given by the composition of general W-operators. We illustrate the algorithm

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978-3-031-57792-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

Vertebra

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/281109.jpg

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Chapter 8: The Give-Back of the Givingets of ., especially Euclidean balls. We shed light on the hierarchical structure of these rotations, induced by their discontinuity (characterized by hinge angles) and the size of the considered ball. We propose efficient algorithmic schemes leading to the construction of combinatorial models (tree

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Plato killed a moth in my dreamlly, naive arithmetic hyperplanes, and we present a new approach to recognise these discrete structures based on the Stern-Brocot tree. The algorithm for DSS recognition proposes an alternative method to the state of the art, keeping the linear complexity and incremental character. While most of the

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Chapter 7: Ajahn Chah Gives a Teachingquence of three 2D shears along coordinate axes, leading to a decomposition of a 3D rotation into nine (beam) shears in total. We define a 3D digitized rotation using nine digitized beam shears, i.e., we round the result of each shear before applying the next one. As digitized shears are bijective,