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Titlebook: Computer Vision and Image Processing; 5th International Co Satish Kumar Singh,Partha Roy,P. Nagabhushan Conference proceedings 2021 The Edi

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樓主: Alacrity
31#
發(fā)表于 2025-3-26 22:51:46 | 只看該作者
32#
發(fā)表于 2025-3-27 04:12:54 | 只看該作者
U-Net-Based Approach for Segmentation of Tables from Scanned Pages,odel is verified by testing the proposed system on the ICDAR 2013, ICDAR 2019, Marmot datasets and some randomly clicked images. Our model outperforms all the other methods presented in ICDAR 2019 table segmentation competition with an F score of 0.9694.
33#
發(fā)表于 2025-3-27 08:13:01 | 只看該作者
,Camera Based Parking Slot Detection for?Autonomous Parking,ikes, cones, carton boxes and trees, this method achieved a promising performance with F1 score higher than 97%. With the ability to run on low computational devices such as CPU, this method is adaptable to practical solutions for both AD and aftermarket ADAS systems.
34#
發(fā)表于 2025-3-27 12:48:51 | 只看該作者
35#
發(fā)表于 2025-3-27 17:30:30 | 只看該作者
36#
發(fā)表于 2025-3-27 20:09:33 | 只看該作者
37#
發(fā)表于 2025-3-27 22:23:34 | 只看該作者
38#
發(fā)表于 2025-3-28 03:10:03 | 只看該作者
The Definitive Guide to MongoDBts on caricature recognition dataset and subsequent comparison of our proposed network against the baseline model quantitatively substantiates our hypothesis. While comparing the performance of our modified network against the baseline, we were able to improve the recognition accuracy by . for . setting and by . for . setting.
39#
發(fā)表于 2025-3-28 09:41:37 | 只看該作者
Eelco Plugge,Peter Membrey,Tim Hawkinse maps in the final stage. Experimental results on the benchmark KITTI dataset show that the proposed modifications outperform the existing VoxelNet based models and other fusion based methods in terms of accuracy as well as time.
40#
發(fā)表于 2025-3-28 12:38:40 | 只看該作者
Deep Learning-Based Smart Parking Management System and Business Model,nd updated automatically. Billing for the parking space usage will also be done automatically as per the regulated guidelines. Raspberry Pi and deep learning tools are used for the implementation. The proposed system is cost-effective and reduces time and energy.
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