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pro vyhledávání: '"Error map"'
Image quality assessment is a fundamental problem in the field of image processing, and due to the lack of reference images in most practical scenarios, no-reference image quality assessment (NR-IQA), has gained increasing attention recently. With th
Externí odkaz:
http://arxiv.org/abs/2305.09353
Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering pre-operative images invalid. As a cost-effective and portable tool,
Externí odkaz:
http://arxiv.org/abs/2308.10784
Akademický článek
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Akademický článek
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Publikováno v:
Photonics, Vol 11, Iss 2, p 125 (2024)
This paper introduces an optimization strategy for fabricating large aspheric mirrors. We polished a large SiC aspheric mirror, 4 m in diameter, achieving a surface error of 1/40λ RMS. To the best of our knowledge, this is the first instance of such
Externí odkaz:
https://doaj.org/article/2f491951f6ea46f1a6c441ab4fd6b3c5
This work aims to enable on-device training of convolutional neural networks (CNNs) by reducing the computation cost at training time. CNN models are usually trained on high-performance computers and only the trained models are deployed to edge devic
Externí odkaz:
http://arxiv.org/abs/2007.03213
Publikováno v:
Pattern Recognition PR_108515 ,2022
Medical image segmentation is usually regarded as one of the most important intermediate steps in clinical situations and medical imaging research. Thus, accurately assessing the segmentation quality of the automatically generated predictions is esse
Externí odkaz:
http://arxiv.org/abs/2006.14345
Publikováno v:
In Pattern Recognition May 2022 125
Autor:
Zhang, Rongzhao, Chung, Albert C. S.
When introducing advanced image computing algorithms, e.g., whole-heart segmentation, into clinical practice, a common suspicion is how reliable the automatically computed results are. In fact, it is important to find out the failure cases and identi
Externí odkaz:
http://arxiv.org/abs/1907.12244
This work proposes a method for depth completion of sparse LiDAR data using a convolutional neural network which can be used to generate semi-dense depth maps and "almost" full 3D point-clouds with significantly lower root mean squared error (RMSE) o
Externí odkaz:
http://arxiv.org/abs/1907.10148