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Titlebook: Computer Vision and Image Processing; 8th International Co Harkeerat Kaur,Vinit Jakhetiya,Sanjeev Kumar Conference proceedings 2024 The Edi

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樓主: T-Lymphocyte
51#
發(fā)表于 2025-3-30 09:58:21 | 只看該作者
Damage Segmentation and Restoration of Ancient Wall Paintings for Preserving Cultural Heritage,rating due to the passage of time, environmental factors, and human actions. Preserving and Restoring these delicate artworks is crucial. One approach to aid their digital restoration is leveraging advanced technologies like deep learning. This study applies image segmentation and restoration techni
52#
發(fā)表于 2025-3-30 13:21:38 | 只看該作者
53#
發(fā)表于 2025-3-30 17:39:07 | 只看該作者
,Fusion of?Handcrafted Features and?Deep Features to?Detect COVID-19,ures and handcrafted features to provide a unique method for COVID-19 identification using chest X-rays. In order to extract high-level features from the chest X-ray pictures, we first use a convolutional neural network (CNN) that has already been trained to take advantage of deep learning. The disc
54#
發(fā)表于 2025-3-30 23:49:04 | 只看該作者
,An Improved AttnGAN Model for?Text-to-Image Synthesis, text sequence length increases, these models suffer from a loss of information, leading to missed keywords and unsatisfactory results. To address this, we propose an attentional GAN (AttnGAN) model with a text attention mechanism. We evaluate AttnGAN variants on the MS-COCO dataset qualitatively an
55#
發(fā)表于 2025-3-31 01:41:34 | 只看該作者
56#
發(fā)表于 2025-3-31 05:21:36 | 只看該作者
,MAAD-GAN: Memory-Augmented Attention-Based Discriminator GAN for?Video Anomaly Detection,troduces a novel approach, named MAAD-GAN, for video anomaly detection (VAD) utilizing Generative Adversarial Networks (GANs). The MAAD-GAN framework combines a Wide Residual Network (WRN) in the generator with a memory module to learn the normal patterns present in the training video dataset, enabl
57#
發(fā)表于 2025-3-31 11:10:14 | 只看該作者
,AG-PDCnet: An Attention Guided Parkinson’s Disease Classification Network with?MRI, DTI and?Clinican Guided multi-class multi-modal PD Classification framework. In particular, we combine clinical assessments with the Neuroimaging data, namely, MRI and DTI. The three classes considered for this problem are PD, Healthy Controls (HC) and Scans Without Evidence of Dopamine Deficiency (SWEDD). Four CN
58#
發(fā)表于 2025-3-31 14:58:58 | 只看該作者
59#
發(fā)表于 2025-3-31 21:35:29 | 只看該作者
60#
發(fā)表于 2025-4-1 01:00:25 | 只看該作者
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