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

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书目名称Computer Vision and Image Processing影响因子(影响力)




书目名称Computer Vision and Image Processing影响因子(影响力)学科排名




书目名称Computer Vision and Image Processing网络公开度




书目名称Computer Vision and Image Processing网络公开度学科排名




书目名称Computer Vision and Image Processing被引频次




书目名称Computer Vision and Image Processing被引频次学科排名




书目名称Computer Vision and Image Processing年度引用




书目名称Computer Vision and Image Processing年度引用学科排名




书目名称Computer Vision and Image Processing读者反馈




书目名称Computer Vision and Image Processing读者反馈学科排名

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Robust Semi-supervised Medical Image Classification: Leveraging Reliable Pseudo-labels,te our model’s efficacy and adaptability under various test scenarios. Comparative results, showing a consistent performance improvement over other SSL methods, underline the potential of our approach in redefining boundaries in semi-supervised medical image classification tasks, highlighting its pr

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Cross-Domain Feature Extraction Using CycleGAN for Large FoV Thermal Image Creation,ation, making identifying and matching robust features and descriptors difficult. To enhance feature extraction, we adopt the Cycle Generative Adversarial Network (CycleGAN) technique to convert thermal images into their RGB counterparts prior to feature extraction. This enables us to leverage the r

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,Federated Scaling of Pre-trained Models for Deep Facial Expression Recognition,ensive experimentation using standard pre-trained vision models (ResNet-50, VGG-16, Xception, Vision Transformers) and benchmark datasets (CK+, FERG, FER-2013, JAFFE, MUG), this paper presents interesting perspectives for future research in the direction of federated Deep FER.

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,Colorization of Thermal Facial Images into Visible Facial Image Using RGB-GAN, translate or map from the one domain image into another domain image the cycle GAN fits with its application. CycleGAN aims to acquire knowledge of the relationship between two distinct image collections originating from separate domains, each possessing unique styles, textures, or visual attribute

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