Zobrazeno 1 - 10
of 117
pro vyhledávání: '"Mousavi, Sara"'
Semantic segmentation is a challenging computer vision task demanding a significant amount of pixel-level annotated data. Producing such data is a time-consuming and costly process, especially for domains with a scarcity of experts, such as medicine
Externí odkaz:
http://arxiv.org/abs/2202.11900
Annotating images for semantic segmentation requires intense manual labor and is a time-consuming and expensive task especially for domains with a scarcity of experts, such as Forensic Anthropology. We leverage the evolving nature of images depicting
Externí odkaz:
http://arxiv.org/abs/2105.09975
Domain-specific image collections present potential value in various areas of science and business but are often not curated nor have any way to readily extract relevant content. To employ contemporary supervised image analysis methods on such image
Externí odkaz:
http://arxiv.org/abs/2003.04261
Autor:
Dey, Tapajit, Mousavi, Sara, Ponce, Eduardo, Fry, Tanner, Vasilescu, Bogdan, Filippova, Anna, Mockus, Audris
Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tool
Externí odkaz:
http://arxiv.org/abs/2003.03172
Large collections of images, if curated, drastically contribute to the quality of research in many domains. Unsupervised clustering is an intuitive, yet effective step towards curating such datasets. In this work, we present a workflow for unsupervis
Externí odkaz:
http://arxiv.org/abs/2001.05845
Autor:
Bahari, Hossein, Taheri, Shaghayegh, Rashidmayvan, Mohammad, Hezaveh, Zohreh Sajadi, Mousavi, Sara Ebrahimi, Malekahmadi, Mahsa
Publikováno v:
In PharmaNutrition September 2023 25
Image collections, if critical aspects of image content are exposed, can spur research and practical applications in many domains. Supervised machine learning may be the only feasible way to annotate very large collections, but leading approaches rel
Externí odkaz:
http://arxiv.org/abs/1902.10848
Autor:
Nau, Anna‐Maria, Mousavi, Sara, Lee, Dylan, Hossain, Rayhan, Griffin, Tatianna, Steadman, Dawnie Wolfe, Mockus, Audris
Publikováno v:
Journal of Forensic Sciences; Sep2024, Vol. 69 Issue 5, p1671-1680, 10p
Autor:
Hemmati, Amirhossein, Ghoreishy, Seyed Mojtaba, Karami, Keianoush, Imani, Hossein, Farsani, Gholamreza Mohammadi, Mousavi, Sara Ebrahimi, Asoudeh, Farzaneh, Shariati-Bafghi, Seyedeh-Elaheh, Karamati, Mohsen
Publikováno v:
In Clinical Nutrition ESPEN December 2021 46:271-275
Autor:
Mousavi, Sara, Heidari, Alireza, Safarzadeh, Sahar, Asgari, Parviz, Shoushtari, Marzieh Talebzadeh
Publikováno v:
Women's Health Bulletin; Jul2024, Vol. 11 Issue 3, p145-152, 8p