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Titlebook: Smart Multimedia; Third International Stefano Berretti,Guan-Ming Su Conference proceedings 2022 The Editor(s) (if applicable) and The Auth

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樓主: intern
21#
發(fā)表于 2025-3-25 05:39:50 | 只看該作者
22#
發(fā)表于 2025-3-25 07:58:32 | 只看該作者
IARG: Improved Actor Relation Graph Based Group Activity Recognitionild the actor relationship graph to allow the graph convolution network to learn how to classify group activities. We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on the public group activity recognition datasets called collective activity dataset and
23#
發(fā)表于 2025-3-25 14:30:24 | 只看該作者
Infrared and Visible Image Fusion Based on Multi-scale Gaussian Rolling Guidance Filter Decompositioespectively. Then, the three different scale layers are respectively fused based on the properties of different scale layers through spatial frequency-based, gradient-based and energy-based fusion strategies. Finally, the final fusion result is obtained by adding the fusion results of the three diff
24#
發(fā)表于 2025-3-25 19:51:47 | 只看該作者
25#
發(fā)表于 2025-3-25 23:58:26 | 只看該作者
Gamified Smart Grid Implementation Through Pico, Nano, and Microgrids in a Sustainable Campust of things (IoT) applications provide advanced monitoring and control in the smart grid in case of an outage or disturbances. Therefore, this paper presents a microgrid, nanogrid, and picogrid integration using a building facility at Tecnologico de Monterrey, Mexico City Campus. Besides, a solar ph
26#
發(fā)表于 2025-3-26 01:40:46 | 只看該作者
A Real-Time Fall Classification Model Based on?Frame Series Motion Deformationem, including viewing direction of the camera and illumination condition of the environment is quite challenging. The problem would be even more serious when the training dataset does not have representative features as the surveillance area. In this paper, we propose a robust, real-time, CV based f
27#
發(fā)表于 2025-3-26 06:03:10 | 只看該作者
GradXcepUNet: Explainable AI Based Medical Image Segmentationduce the final segmentation results. With the assistance of XAI analysis and visualization, our GradXcepUNet outperforms the original U-Net and many state-of-the-art methods. The evaluation results show that we can reach a Dice coefficient of 97.73% and an Intersection over Union (IoU) score of 78.8
28#
發(fā)表于 2025-3-26 09:32:04 | 只看該作者
3D Segmentation and?Visualization of?Human Brain CT Images for?Surgical Training - A VTK Approachnvironment for Ventricular puncture operation planning and training. The difference between our work and other segmentation techniques is that we need to segment not only one target, but also the path along the surgical tool inserted into the brain. This creates challenges to the algorithm design be
29#
發(fā)表于 2025-3-26 13:50:50 | 只看該作者
30#
發(fā)表于 2025-3-26 20:53:02 | 只看該作者
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