標題: Titlebook: Computer Vision and Image Processing; 8th International Co Harkeerat Kaur,Vinit Jakhetiya,Sanjeev Kumar Conference proceedings 2024 The Edi [打印本頁] 作者: T-Lymphocyte 時間: 2025-3-21 18:47
書目名稱Computer Vision and Image Processing影響因子(影響力)
書目名稱Computer Vision and Image Processing影響因子(影響力)學科排名
書目名稱Computer Vision and Image Processing網(wǎng)絡公開度
書目名稱Computer Vision and Image Processing網(wǎng)絡公開度學科排名
書目名稱Computer Vision and Image Processing被引頻次
書目名稱Computer Vision and Image Processing被引頻次學科排名
書目名稱Computer Vision and Image Processing年度引用
書目名稱Computer Vision and Image Processing年度引用學科排名
書目名稱Computer Vision and Image Processing讀者反饋
書目名稱Computer Vision and Image Processing讀者反饋學科排名
作者: Fibrin 時間: 2025-3-21 22:37
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作者: Dysplasia 時間: 2025-3-22 04:12 作者: 能得到 時間: 2025-3-22 06:49 作者: 遠地點 時間: 2025-3-22 09:38
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作者: pericardium 時間: 2025-3-22 15:26 作者: pericardium 時間: 2025-3-22 21:05
,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.作者: 沒有貧窮 時間: 2025-3-22 23:27 作者: 茁壯成長 時間: 2025-3-23 03:42
,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作者: Extricate 時間: 2025-3-23 06:46 作者: 跑過 時間: 2025-3-23 11:18 作者: 個阿姨勾引你 時間: 2025-3-23 14:44 作者: glucagon 時間: 2025-3-23 18:54 作者: 過份艷麗 時間: 2025-3-23 23:38 作者: 多骨 時間: 2025-3-24 03:52 作者: surrogate 時間: 2025-3-24 07:39
Existential Theory of the Reals,ated images. Careful selection of these initial values proved crucial in achieving accurate and visually appealing inpainted results. Furthermore, we explored various useful loss functions that can be employed within the model. We discovered that the choice of loss function also has a substantial ef作者: Yourself 時間: 2025-3-24 10:53 作者: 過渡時期 時間: 2025-3-24 15:37
https://doi.org/10.1007/978-3-030-81885-2erimental results show that the suggested semi-GAN approach, when applied to the PolypsSet and SUN datasets, produced a classification accuracy of 72.85% and 74.12% respectively. This represents a significant improvement over existing methods and highlights the potential of GANs in the medical image作者: 滔滔不絕地講 時間: 2025-3-24 22:46
Clique, Independent Set, and Vertex Coverr pruning approach and demonstrate its efficacy by reducing the number of parameters and floating point operations while maintaining the mean Intersection over Union (mIoU) metric. We conduct experiments on two widely accepted semantic segmentation architectures: UNet and ERFNet. Our experiments and作者: LATHE 時間: 2025-3-24 23:47 作者: 品嘗你的人 時間: 2025-3-25 05:32 作者: 指令 時間: 2025-3-25 09:48 作者: Medicare 時間: 2025-3-25 14:55
Variables, Constants, Scopes, and Modulesutput image. Extensive comparisons with different segmentation models show that the proposed approach outperforms the rest with an mIOU of 0.892. The proposed method also demonstrates remarkable inpainting results with an SSIM score of 0.9812 on test images. Results show that the method achieves pro作者: affinity 時間: 2025-3-25 17:49
Variables, Constants, Scopes, and Modules 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作者: 職業(yè) 時間: 2025-3-25 23:49
An Introduction to the Julia Language the performance of the classification with the standalone applications of convolutional and handcrafted features, we find that combining the features in our innovative framework enhances performance.作者: 痛打 時間: 2025-3-26 03:33
Variables, Constants, Scopes, and Modulesor research and application on the Real-world Affective Faces Database (RAF-DB) dataset. To assess the effectiveness of the suggested approach, we performed experiments employing three distinct FER models (EfficientFace, MA-Net, and POSTER) to validate the performance. We evaluated the performance o作者: 維持 時間: 2025-3-26 07:06 作者: Moderate 時間: 2025-3-26 10:39
Lecture Notes in Computer Scienceution Neural Network (RCNN) with backbones such as ResNet50, MobileNetv2, and ResNet101, and the results are compared. The result gives a trade-off between the single-stage and two-stage object detection models. The precision of YOLOv7 is 0.986 and 0.966 for normal and kidney stones, respectively, b作者: 持久 時間: 2025-3-26 15:01 作者: Nostalgia 時間: 2025-3-26 19:25 作者: 抵押貸款 時間: 2025-3-27 00:11
Conference proceedings 2024re carefully reviewed and selected from?461?submissions.?The papers focus on?various important and emerging topics in image processing, computer vision applications, deep learning, and machine learning techniques in the domain..作者: 不足的東西 時間: 2025-3-27 03:34 作者: dandruff 時間: 2025-3-27 07:57
Ordinary Differential Equationsd quantitatively. For the image quality analysis, we utilize performance measures such as FID score, R-precision, and IS score. Our results show that the proposed model outperforms existing approaches, producing more realistic images by preserving vital information in the input sequence.作者: Spinal-Fusion 時間: 2025-3-27 11:27 作者: adj憂郁的 時間: 2025-3-27 16:00 作者: cleaver 時間: 2025-3-27 19:06
Lecture Notes in Computer Scienceodels, four CNNs and the XGboost, are fused with an optimal weighted average fusion (OWAF) technique. Publicly available PPMI database is used for evaluation, yielding an accuracy of 96.93% for the three-class classification. Extensive comparisons, including ablation studies, are conducted to validate the effectiveness of our proposed solution.作者: 價值在貶值 時間: 2025-3-27 22:09
A Comparative Study on Deep CNN Visual Encoders for Image Captioning,rent visual encoding methods employed in the model. We have analyzed and compared the performance of six different pre-trained CNN visual encoding models using Bilingual Evaluation Understudy (BLEU) scores.作者: Cholagogue 時間: 2025-3-28 02:59
,MAAD-GAN: Memory-Augmented Attention-Based Discriminator GAN for?Video Anomaly Detection,amples, ensuring that anomalous samples are distorted when reconstructed. Experimental evaluations show the effectiveness of MAAD-GAN as compared to traditional methods on UCSD (University of California, San Diego) Peds2, CUHK Avenue, and ShanghaiTech datasets.作者: Ordnance 時間: 2025-3-28 06:47
,AG-PDCnet: An Attention Guided Parkinson’s Disease Classification Network with?MRI, DTI and?Clinicaodels, four CNNs and the XGboost, are fused with an optimal weighted average fusion (OWAF) technique. Publicly available PPMI database is used for evaluation, yielding an accuracy of 96.93% for the three-class classification. Extensive comparisons, including ablation studies, are conducted to validate the effectiveness of our proposed solution.作者: palpitate 時間: 2025-3-28 13:12 作者: stress-response 時間: 2025-3-28 17:01
Conference proceedings 2024sion and Image Processing, CVIP 2023, held in Jammu, India, during November 3–5, 2023.?..The 140 revised full papers presented in these proceedings were carefully reviewed and selected from?461?submissions.?The papers focus on?various important and emerging topics in image processing, computer visio作者: 安心地散步 時間: 2025-3-28 20:59
,An Improved AttnGAN Model for?Text-to-Image Synthesis,d quantitatively. For the image quality analysis, we utilize performance measures such as FID score, R-precision, and IS score. Our results show that the proposed model outperforms existing approaches, producing more realistic images by preserving vital information in the input sequence.作者: Morbid 時間: 2025-3-29 02:57
,Face Image Inpainting Using Context Encoders and?Dynamically Initialized Mask,ability to recover clear face images from occluded face images has found applications in various domains. One prominent approach in this context is the utilization of autoencoders within the framework of Generative Adversarial Networks (GAN), such as the Context Encoder (CE). The CE is an unsupervis作者: 不透氣 時間: 2025-3-29 04:17
A Comparative Study on Deep CNN Visual Encoders for Image Captioning,he integration of computer vision and natural language processing technology. Despite the fact that numerous techniques for generating image captions have been developed, the result is inadequate and the need for research in this area is still a demanding topic. The human process of describing any i作者: Externalize 時間: 2025-3-29 08:40 作者: ANTE 時間: 2025-3-29 12:08
,Semi-supervised Polyp Classification in?Colonoscopy Images Using GAN,an effective treatment as unnecessary surgeries and ignorance of any potential cancer could be the situations of concern. Thus, classifying any detected lesion as an adenoma (with the potential to become cancerous) or hyperplastic (non-cancerous) is necessary. In recent years, such classification ta作者: myelography 時間: 2025-3-29 17:00 作者: IDEAS 時間: 2025-3-29 23:37
Cross-Domain Feature Extraction Using CycleGAN for Large FoV Thermal Image Creation,oV) in thermal imaging systems often results in insufficient and fragmented large Field of View (FoV) simulation data, thereby compromising the effectiveness of defense applications using large Field of View (FoV) cameras. To address this challenge, we propose an innovative approach employing image 作者: CBC471 時間: 2025-3-30 01:09
,Classification of?Insect Pest Using Transfer Learning Mechanism,ps in the agricultural sector. The identification of crop pests is a difficult problem since, pest infestations cause significant crop damage and quality degradation. The majority of insect species are quite similar to one another that makes the task of detection of the insect on field crops like ri作者: 背信 時間: 2025-3-30 08:06
,Federated Scaling of?Pre-trained Models for?Deep Facial Expression Recognition,al data and the rise in data privacy concerns. Federated learning has emerged as a promising solution for such problems, which however is communication-inefficient. Recently, pre-trained models have shown effective performance in federated learning setups regarding convergence. In this paper, we ext作者: jovial 時間: 2025-3-30 09:58
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作者: 舊石器時代 時間: 2025-3-30 13:21 作者: Hla461 時間: 2025-3-30 17:39
,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作者: BACLE 時間: 2025-3-30 23:49
,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作者: CAB 時間: 2025-3-31 01:41 作者: coddle 時間: 2025-3-31 05:21
,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作者: 惡意 時間: 2025-3-31 11:10
,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作者: 加強防衛(wèi) 時間: 2025-3-31 14:58 作者: accordance 時間: 2025-3-31 21:35 作者: 領先 時間: 2025-4-1 01:00 作者: 招募 時間: 2025-4-1 04:32
Exploring the Feasibility of PPG for Estimation of Heart Rate Variability: A Mathematical Approach,iogram (ECG) and photoplethysmogram (PPG) based techniques. ECG-based estimation and analysis of Heart Rate Variability (HRV) is a prominent technique used to assess cardiovascular health. However, it is expensive and has little practical application for daily monitoring of HRV. To solve such an iss作者: 導師 時間: 2025-4-1 07:17 作者: 恭維 時間: 2025-4-1 11:49 作者: 悶熱 時間: 2025-4-1 17:43
Clique, Independent Set, and Vertex Coverhe integration of computer vision and natural language processing technology. Despite the fact that numerous techniques for generating image captions have been developed, the result is inadequate and the need for research in this area is still a demanding topic. The human process of describing any i
