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Titlebook: Robotics, Control and Computer Vision; Select Proceedings o Hariharan Muthusamy,János Botzheim,Richi Nayak Conference proceedings 2023 The

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樓主: Polk
41#
發(fā)表于 2025-3-28 15:06:20 | 只看該作者
Recovering Images Using Image Inpainting Techniquess available for the image inpainting tasks and analyzes their effectiveness on established metrics. Navier–Stokes and Telea algorithms achieve PSNR 32 between 34 and SSIM above 0.96 for smaller inpainting tasks. Telea algorithm performs better as compared to Navier–Stokes method.
42#
發(fā)表于 2025-3-28 21:18:36 | 只看該作者
43#
發(fā)表于 2025-3-29 02:05:15 | 只看該作者
44#
發(fā)表于 2025-3-29 04:39:28 | 只看該作者
Challenges and Opportunity for Salient Object Detection in COVID-19 Era: A StudyAcute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus is dangerous for human life and considerably affects human lifestyle. In this paper, we focus on the effect of COVID-19 on salient object detection performance due to certain factors such as humans wearing a face mask that may degrade
45#
發(fā)表于 2025-3-29 08:50:52 | 只看該作者
Human Activity Recognition Using Deep Learningatistics to security-based surveillance. In this work, we have trained CNNs like ResNet50, ResNet101, InceptionV3, and InceptionResNetV2 on a common human activity dataset (Stanford 40) and achieved an accuracy of 80.41% with ResNet101 on image data and an accuracy of 54.16% with ResNet101 on video
46#
發(fā)表于 2025-3-29 11:51:52 | 只看該作者
47#
發(fā)表于 2025-3-29 19:00:45 | 只看該作者
Literature Review for Automatic Detection and Classification of Intracranial Brain Hemorrhage Using categories, namely epidural hemorrhage, subdural hemorrhage, subarachnoid hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. We can distinguish between these subtypes on the basis of the character of bleeding and its location in the brain region. Developments in the field of A
48#
發(fā)表于 2025-3-29 22:36:04 | 只看該作者
49#
發(fā)表于 2025-3-29 23:56:59 | 只看該作者
50#
發(fā)表于 2025-3-30 06:07:06 | 只看該作者
Performance Evaluation of Single Sample Ear Recognition Methodsrecognizing a person. The problem becomes even more complex when a single training sample is available. There have been several methods in the literature for single sample ear recognition. However, which best performs best is not clear. Therefore, in this paper, we perform a comparative study of fiv
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