Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Silvia Biasotti"'
Chanalyzer: A Computational Geometry Approach for the Analysis of Protein Channel Shape and Dynamics
Autor:
Andrea Raffo, Luca Gagliardi, Ulderico Fugacci, Luca Sagresti, Simone Grandinetti, Giuseppe Brancato, Silvia Biasotti, Walter Rocchia
Publikováno v:
Frontiers in Molecular Biosciences, Vol 9 (2022)
Morphological analysis of protein channels is a key step for a thorough understanding of their biological function and mechanism. In this respect, molecular dynamics (MD) is a very powerful tool, enabling the description of relevant biological events
Externí odkaz:
https://doaj.org/article/8de13e92151640d6a04ebd5510e898de
Autor:
Andrea Raffo, Silvia Biasotti
Publikováno v:
Mathematics, Vol 9, Iss 23, p 3084 (2021)
The approximation of curvilinear profiles is very popular for processing digital images and leads to numerous applications such as image segmentation, compression and recognition. In this paper, we develop a novel semi-automatic method based on quasi
Externí odkaz:
https://doaj.org/article/013f4f346cd04434aea97ccea48aca8a
Autor:
Tommaso Sorgente, Fabio Vicini, Stefano Berrone, Silvia Biasotti, Gianmarco Manzini, Michela Spagnuolo
Publikováno v:
Calcolo. 60
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1119df31183346a8d806ea4ea7703b09
https://doi.org/10.1007/s10092-023-00526-4
https://doi.org/10.1007/s10092-023-00526-4
Publikováno v:
Computers & Graphics. 107:A6-A8
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8e95c4f7bb3feb57ee64d7a77c7c42f
https://doi.org/10.1016/j.cag.2022.08.011
https://doi.org/10.1016/j.cag.2022.08.011
Publikováno v:
Computers & Graphics. 102:A14-A16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8e1238706b69930e2d425d66aac9af8
https://doi.org/10.1016/j.cag.2021.10.005
https://doi.org/10.1016/j.cag.2021.10.005
Autor:
Chiara Romanengo, Andrea Raffo, Silvia Biasotti, Bianca Falcidieno, Vlassis Fotis, Ioannis Romanelis, Eleftheria Psatha, Konstantinos Moustakas, Ivan Sipiran, Quang-Thuc Nguyen, Chi-Bien Chu, Khoi-Nguyen Nguyen-Ngoc, Dinh-Khoi Vo, Tuan-An To, Nham-Tan Nguyen, Nhat-Quynh Le-Pham, Hai-Dang Nguyen, Minh-Triet Tran, Yifan Qie, Nabil Anwer
This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds. As simple primitives we mean the classical surface primitives derived from constructive soli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64362241f0c74397e49e3a834118662d
http://arxiv.org/abs/2206.07636
http://arxiv.org/abs/2206.07636
Autor:
Elia Moscoso Thompson, Andrea Ranieri, Silvia Biasotti, Miguel Chicchon, Ivan Sipiran, Minh-Khoi Pham, Thang-Long Nguyen-Ho, Hai-Dang Nguyen, Minh-Triet Tran
This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d85f7252a0ff2d547d67d046f03876cb
http://arxiv.org/abs/2205.13326
http://arxiv.org/abs/2205.13326
Autor:
Andrea Raffo, Silvia Biasotti
Publikováno v:
Computers & graphics 89 (2020): 144–155. doi:10.1016/j.cag.2020.05.004
info:cnr-pdr/source/autori:A. Raffo and S. Biasotti/titolo:Data-driven quasi-interpolant spline surfaces for point cloud approximation/doi:10.1016%2Fj.cag.2020.05.004/rivista:Computers & graphics/anno:2020/pagina_da:144/pagina_a:155/intervallo_pagine:144–155/volume:89
Computers & graphics
info:cnr-pdr/source/autori:A. Raffo and S. Biasotti/titolo:Data-driven quasi-interpolant spline surfaces for point cloud approximation/doi:10.1016%2Fj.cag.2020.05.004/rivista:Computers & graphics/anno:2020/pagina_da:144/pagina_a:155/intervallo_pagine:144–155/volume:89
Computers & graphics
In this paper we investigate a local surface approximation, the Weighted Quasi Interpolant Spline Approximation (wQISA), specifically designed for large and noisy point clouds. We briefly describe the properties of the wQISA representation and introd
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07934897909741293394e735c505dcaa
https://doi.org/10.1016/j.cag.2020.05.004
https://doi.org/10.1016/j.cag.2020.05.004
Publikováno v:
Pattern recognition letters 131 (2020): 405–412. doi:10.1016/j.patrec.2020.01.025
info:cnr-pdr/source/autori:C. Romanengo, S. Biasotti, and B. Falcidieno/titolo:Recognising decorations in archaeological finds through the analysis of characteristic curves on 3D models/doi:10.1016%2Fj.patrec.2020.01.025/rivista:Pattern recognition letters/anno:2020/pagina_da:405/pagina_a:412/intervallo_pagine:405–412/volume:131
info:cnr-pdr/source/autori:C. Romanengo, S. Biasotti, and B. Falcidieno/titolo:Recognising decorations in archaeological finds through the analysis of characteristic curves on 3D models/doi:10.1016%2Fj.patrec.2020.01.025/rivista:Pattern recognition letters/anno:2020/pagina_da:405/pagina_a:412/intervallo_pagine:405–412/volume:131
In the analysis of archaeological finds, it is important for archaeologists to identify their style, origin, period, etc. to allow their correct classification. In the digital era, the development of automatic techniques to measure the peculiar chara
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d8aea7751595fba24dcd98c0a67e98d
https://doi.org/10.1016/j.patrec.2020.01.025
https://doi.org/10.1016/j.patrec.2020.01.025
Publikováno v:
Ital-IA: Secondo Convegno Nazionale CINI sull'Intelligenza Artificiale, Torino (online), 9-11/02/2022
info:cnr-pdr/source/autori:Andrea Ranieri, Silvia Biasotti, Elia Moscoso Thompson, Michela Spagnuolo/congresso_nome:Ital-IA: Secondo Convegno Nazionale CINI sull'Intelligenza Artificiale/congresso_luogo:Torino (online)/congresso_data:9-11%2F02%2F2022/anno:2022/pagina_da:/pagina_a:/intervallo_pagine
info:cnr-pdr/source/autori:Andrea Ranieri, Silvia Biasotti, Elia Moscoso Thompson, Michela Spagnuolo/congresso_nome:Ital-IA: Secondo Convegno Nazionale CINI sull'Intelligenza Artificiale/congresso_luogo:Torino (online)/congresso_data:9-11%2F02%2F2022/anno:2022/pagina_da:/pagina_a:/intervallo_pagine
Questo documento riassume il contributo IMATI allo sviluppo di metodi per il riconoscimento di ammaloramenti del manto stradale, quali buche, crepe, cedimenti attraverso tecniche di deep learning. Tali contributi sono stati sviluppati nel progetto MI
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=cnr_________::73bcdbd9982e062f1bcccb51cb9e0693
https://www.ital-ia2022.it/
https://www.ital-ia2022.it/