Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Rahul Bhotika"'
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6920e1caaa8c1760aad2a7e40e39473a
https://doi.org/10.1109/wacv56688.2023.00226
https://doi.org/10.1109/wacv56688.2023.00226
Autor:
Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, Stefano Soatto
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200588
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb57ff6486d1fc396ff9335deef64781
https://doi.org/10.1007/978-3-031-20059-5_17
https://doi.org/10.1007/978-3-031-20059-5_17
Autor:
Sagnik Das, Kunwar Yashraj Singh, Jon Wu, Erhan Bas, Vijay Mahadevan, Rahul Bhotika, Dimitris Samaras
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09c2797a3ba14653760e800f02fbc776
https://doi.org/10.1109/iccv48922.2021.00423
https://doi.org/10.1109/iccv48922.2021.00423
Autor:
Rahul Bhotika, Orchid Majumder, Alessandro Achille, Avinash Ravichandran, Qing Liu, Stefano Soatto
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030585709
ECCV (7)
ECCV (7)
We propose a method to train a model so it can learn new classification tasks while improving with each task solved. This amounts to combining meta-learning with incremental learning. Different tasks can have disjoint classes, so one cannot directly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15c587e74bcf982db938cb5d979e551f
https://doi.org/10.1007/978-3-030-58571-6_40
https://doi.org/10.1007/978-3-030-58571-6_40
Publikováno v:
ICCV
We propose a method for learning embeddings for few-shot learning that is suitable for use with any number of ways and any number of shots (shot-free). Rather than fixing the class prototypes to be the Euclidean average of sample embeddings, we allow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4037bdb42a5d45474281b8e89cc5a330
Publikováno v:
Lung Imaging and Computer Aided Diagnosis ISBN: 9780429110726
Scopus-Elsevier
Scopus-Elsevier
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef68a6c2c46f926ad20589c341f913e1
https://doi.org/10.1201/b11106-14
https://doi.org/10.1201/b11106-14
Publikováno v:
Medical Imaging: Image Processing
Radiologists are required to read thousands of patient images every day, and any tools that can improve their workflow to help them make efficient and accurate measurements is of great value. Such an interactive tool must be intuitive to use, and we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7c682cdb4cf60c76fda6d7d490fe315
https://doi.org/10.1117/12.2082011
https://doi.org/10.1117/12.2082011
Publikováno v:
Medical Imaging: Image Processing
Minkowski Functionals (MFs) are geometric measurements of 3D shapes, including volume, surface area, curvature and Euler number. MFs can be used as texture descriptors for medical image analysis in the segmentation of normal anatomy as well as in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91d4a993910f0a9e144687adca69abf8
https://doi.org/10.1117/12.912033
https://doi.org/10.1117/12.912033
Publikováno v:
HISB
Organ labeling involves estimating approximate organ locations from medical images and has several traditional and emerging applications. The organ labels can serve as initialization for automatic and fast image processing, provide additional informa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbe6a8bfa21559dca64488fd428f61c7
https://doi.org/10.1109/hisb.2011.38
https://doi.org/10.1109/hisb.2011.38
Autor:
Mark Joshi, Naveen M. Kulkarni, Paulo Mendonça, Darin R. Okerlund, Peter Lamb, Duque de Pinho, Rahul Bhotika, Dushyant V. Sahani
Publikováno v:
Medical Imaging: Image Processing
The feasibility and utility of creating virtual un-enhanced images from contrast enhanced data acquired using a fast switching dual energy CT acquisition, is explored. Utilizing projection based material decomposition data, monochromatic images are g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9efc648b21c4f85daeb22349d3a1260
https://doi.org/10.1117/12.878565
https://doi.org/10.1117/12.878565