Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

A COMPARISON OF METHODS FOR THE APPROXIMATION AND ANALYSIS OF RAINFALL FIELDS IN ENVIRONMENTAL APPLICATIONS

Autor: G. Patané, A. Cerri, V. Skytt, S. Pittaluga, S. Biasotti, D. Sobrero, T. Dokken, M. Spagnuolo

Publikováno v: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol II-3-W5, Pp 523-530 (2015)

Digital environmental data are becoming commonplace and the amount of information they provide is huge, yet complex to process, due to the size, variety, and dynamic nature of the data captured by the available sensing devices. Making use of the data

Externí odkaz: https://doaj.org/article/a2192e074c0242149ee31a5443e82530

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Hough Transform for Detecting Space Curves in Digital 3D Models

Autor: C. Romanengo, S. Biasotti, B. Falcidieno

Publikováno v: Journal of mathematical imaging and vision (Dordr., Online) (2022). doi:10.1007/s10851-021-01066-8
info:cnr-pdr/source/autori:C. Romanengo, S. Biasotti and B. Falcidieno/titolo:Hough Transform for Detecting Space Curves in Digital 3D Models/doi:10.1007%2Fs10851-021-01066-8/rivista:Journal of mathematical imaging and vision (Dordr., Online)/anno:2022/pagina_da:/pagina_a:/intervallo_pagine:/volume

We present and analyse the Hough transform (HT) to recognise and approximate space curves in digital models, a problem that is not currently addressed by the standard HT. Our method works on meshes and point clouds and applies to models even incomple

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=cnr_________::42575ceb3bd932901a13bc818c8f405a
https://link.springer.com/article/10.1007%2Fs10851-021-01066-8

Automatic Segmentation of Archaeological Fragments with Relief Patterns using Convolutional Neural Networks

Autor: E. Moscoso Thompson, A. Ranieri, S. Biasotti

Publikováno v: EUROGRAPHICS Workshop on Graphics and Cultural Heritage (2021), pp. 93–102, Bournemouth, UK, November 4-6, 2021
info:cnr-pdr/source/autori:E. Moscoso Thompson, A. Ranieri, S. Biasotti/congresso_nome:EUROGRAPHICS Workshop on Graphics and Cultural Heritage (2021)/congresso_luogo:Bournemouth, UK/congresso_data:November 4-6, 2021/anno:2021/pagina_da:93/pagina_a:102/intervallo_pagine:93–102

The recent commodification of high-quality 3D scanners is leading to the possibility of capturing models of archaeological finds and automatically recognizing their surface reliefs. We present our advancements in this field using Convolutional Neural

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b637f31d6314bb0fde9f6b0e67f1959