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Titlebook: Computer Vision and Image Processing; 7th International Co Deep Gupta,Kishor Bhurchandi,Sanjeev Kumar Conference proceedings 2023 The Edito

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樓主: Radiofrequency
31#
發(fā)表于 2025-3-26 22:49:11 | 只看該作者
32#
發(fā)表于 2025-3-27 02:18:26 | 只看該作者
33#
發(fā)表于 2025-3-27 06:27:26 | 只看該作者
Private Multiplication over Finite Fieldslandmarks, which conditions the network to produce template-shaped object segments. The performance of the proposed method was evaluated with . and . measures on the HELEN data set for lip segmentation. We observed perceptually superior segments with smooth object boundaries when compared to state-of-the-art techniques.
34#
發(fā)表于 2025-3-27 11:59:06 | 只看該作者
35#
發(fā)表于 2025-3-27 16:29:54 | 只看該作者
,Anomaly Detection in?ATM Vestibules Using Three-Stream Deep Learning Approach,ataset for finetuning object detection models to detect ATM class and temporal annotated video dataset to train the model for video anomaly detection in ATM vestibule. The presented work achieves a recall score of 0.93, and false positive rate of 0.13.
36#
發(fā)表于 2025-3-27 19:28:17 | 只看該作者
37#
發(fā)表于 2025-3-27 23:26:26 | 只看該作者
,Share-GAN: A Novel Shared Task Training in?Generative Adversarial Networks for?Data Hiding,owards attacks like Gaussian blurring, rotation, noise, and cropping. However, the model can be trained on any possible attacks to reduce noise sensitivity further. In this manuscript, we considered images as both messages and containers. However, the method can be extended to any combination of multi-media data.
38#
發(fā)表于 2025-3-28 03:41:45 | 只看該作者
,FlashGAN: Generating Ambient Images from?Flash Photographs, discriminator is employed to classify patches from each image as real or generated and penalize the network accordingly. Experimental results demonstrate that the proposed architecture significantly outperforms the current state-of-the-art, performing even better on facial images with homogenous backgrounds.
39#
發(fā)表于 2025-3-28 09:10:58 | 只看該作者
40#
發(fā)表于 2025-3-28 10:40:24 | 只看該作者
DeepTemplates: Object Segmentation Using Shape Templates,landmarks, which conditions the network to produce template-shaped object segments. The performance of the proposed method was evaluated with . and . measures on the HELEN data set for lip segmentation. We observed perceptually superior segments with smooth object boundaries when compared to state-of-the-art techniques.
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