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

彻底明白

https://doi.org/10.1007/978-3-030-05958-3ected and even simply connected whatever the dimension, while remaining digitally convex in the usual sense. Several characterizations were proposed in former works, either based on lattice intersection enumeration with several convex hulls, or using the idempotence of an envelope operator. We conti

Harass

Avant-garde Women and Women’s Suffrage. Up to translation and symmetry, they are completely determined by a normal vector .. Excepted for a few well-identified cases, it is shown that there are two approximations . of ., satisfying ., such that the discrete plane of normal . can be partitioned into two sets having respectively the combi

臭名昭著

The Conservative Party and Women’s Suffrageong other applications, the maximum distance transform (DT) value can describe the thickness of the connected components of the image. In this paper, we propose using the maximum distance transform value as an attribute of component tree nodes. We present a novel algorithm to compute the maximum DT

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Synapse

Josefina Erikson,Lenita Freidenvallnal problems onto them. However digital surfaces (boundary of voxels) cannot benefit directly from the classical mesh calculus frameworks, since their vertex and face geometry is too poor to capture the geometry of the underlying smooth Euclidean surface well enough. This paper proposes two new calc

Modify

https://doi.org/10.1007/978-3-031-52359-5ht to one of the two focal points. Fitting of egg-shapes thus requires optimization of 6 parameters. Our approach simplifies this to a 1D parameter space exploration. First, we utilize a least square algorithm to fit an ellipse to the boundary. While the desired egg-shape shares the orientation of t

dura-mater

宽容

Sugarcane Biofuel Production in Indonesiaished on the subject and the literature keeps increasing. This paper aims to fill the lack of morphological approaches on the matter. We propose a set of group equivariant layers using morphological operators, several model configurations are tested and compared with a convolutional equivalent netwo

Obverse

https://doi.org/10.1007/978-3-642-73875-3o date has been the morphological neural networks (MNN). Although MNN have performed better than CNN in solving some problems, they inherit their black-box nature. Furthermore, in the case of binary images, they are approximations that loose the Boolean lattice structure of MM operators and, thus, i

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