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Titlebook: Computer Vision and Image Processing; 4th International Co Neeta Nain,Santosh Kumar Vipparthi,Balasubramanian Conference proceedings 2020 S

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41#
發(fā)表于 2025-3-28 14:55:20 | 只看該作者
Video Colorization Using CNNs and Keyframes Extraction: An Application in Saving Bandwidthon these colored frames. Through the extracted keyframes we get a good knowledge of the colors that have been used in the video which helps us in colorizing the grayscale version of the video efficiently.
42#
發(fā)表于 2025-3-28 21:38:17 | 只看該作者
43#
發(fā)表于 2025-3-29 02:09:16 | 只看該作者
https://doi.org/10.1007/978-1-4302-2642-0 of the images are also used, as input to the proposed multi channel CNN architecture. The experiments reported on widely used AVA database show improvement in the aesthetic assessment performance over existing approaches.
44#
發(fā)表于 2025-3-29 05:13:03 | 只看該作者
https://doi.org/10.1007/978-1-4302-1894-4l exam. A dataset of ECG recordings of 130 students was taken during the oral exam. A customized CNN is designed for stress recognition, and it has achieved 97.22% and 93.10% stress detection accuracy for filtered and noisy datasets, respectively.
45#
發(fā)表于 2025-3-29 07:34:06 | 只看該作者
1865-0929 mage Processing. held in?Jaipur, India, in September 2019.?.The 73 full papers and 10 short papers were carefully reviewed and selected from 202 submissions. The papers are organized by the topical headings in two parts.?.Part I: Biometrics;? Computer Forensic; Computer Vision; Dimension Reduction;
46#
發(fā)表于 2025-3-29 14:05:52 | 只看該作者
Image Aesthetics Assessment Using Multi Channel Convolutional Neural Networks of the images are also used, as input to the proposed multi channel CNN architecture. The experiments reported on widely used AVA database show improvement in the aesthetic assessment performance over existing approaches.
47#
發(fā)表于 2025-3-29 17:59:42 | 只看該作者
Psychological Stress Detection Using Deep Convolutional Neural Networksl exam. A dataset of ECG recordings of 130 students was taken during the oral exam. A customized CNN is designed for stress recognition, and it has achieved 97.22% and 93.10% stress detection accuracy for filtered and noisy datasets, respectively.
48#
發(fā)表于 2025-3-29 21:25:18 | 只看該作者
49#
發(fā)表于 2025-3-30 03:44:25 | 只看該作者
50#
發(fā)表于 2025-3-30 06:21:11 | 只看該作者
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