標題: Titlebook: Computer Vision and Image Processing; 7th International Co Deep Gupta,Kishor Bhurchandi,Sanjeev Kumar Conference proceedings 2023 The Edito [打印本頁] 作者: Radiofrequency 時間: 2025-3-21 16:59
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(luò)公開度
書目名稱Computer Vision and Image Processing網(wǎng)絡(luò)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: 技術(shù) 時間: 2025-3-21 20:43
Advances in Cryptology – CRYPTO 2016orrectly. The second dataset consists of masked faces and faces without masks. To validate the generalization capability of the proposed model, the trained model is tested on two new standard datasets. In addition to that, the testing is done on a dataset created by ourselves. The proposed model per作者: 格子架 時間: 2025-3-22 03:09 作者: Legend 時間: 2025-3-22 05:35 作者: hankering 時間: 2025-3-22 12:39 作者: 串通 時間: 2025-3-22 14:51 作者: 串通 時間: 2025-3-22 17:36 作者: 討厭 時間: 2025-3-22 21:43 作者: 不易燃 時間: 2025-3-23 03:13 作者: 著名 時間: 2025-3-23 07:59 作者: CHIP 時間: 2025-3-23 10:09
,An Explainable Transfer Learning Based Approach for?Detecting Face Mask,orrectly. The second dataset consists of masked faces and faces without masks. To validate the generalization capability of the proposed model, the trained model is tested on two new standard datasets. In addition to that, the testing is done on a dataset created by ourselves. The proposed model per作者: lactic 時間: 2025-3-23 17:32 作者: ORBIT 時間: 2025-3-23 18:36
,A Segmentation Based Robust Fractional Variational Model for?Motion Estimation, which is a union of disjoint and independently moving regions such that each motion region contains objects of equal flow velocity. The resulting fractional order partial differential equations are numerically discretized using Grünwald–Letnikov fractional derivative. The nonlinear formulation is t作者: 眨眼 時間: 2025-3-23 23:06 作者: 調(diào)整校對 時間: 2025-3-24 02:40
,CandidNet: A Novel Framework for?Candid Moments Detection,id). The scoring mechanism allows us to compare images based on their candidness. A detailed ablation study conducted on the proposed framework with various configurations proves the efficacy of the method with a classification accuracy of 92% on CELEBA-HQ [.] and 94% on CANDID-SCORE [.]. With a hig作者: 信徒 時間: 2025-3-24 10:33
,Cost Efficient Defect Detection in?Bangle Industry Using Transfer Learning,-labeled images collected from one of the bangle factories, which act as a seed to train the network which can detect common defects. We present an extensive evaluation of performance of various machine learning algorithms on our dataset using traditional features, and features extracted from popula作者: BROOK 時間: 2025-3-24 11:48 作者: Lacerate 時間: 2025-3-24 17:12 作者: 貧困 時間: 2025-3-24 20:19 作者: intricacy 時間: 2025-3-25 02:03
https://doi.org/10.1007/978-3-662-53018-4ge. (2) An extractor that reverse-engineers the embedder function to extract the hidden data inside the encoded image. A multi-discriminator GAN framework with multi-objective training for multimedia hiding is one of the novel contributions of this work.作者: 傻瓜 時間: 2025-3-25 04:20
Advances in Cryptology – CRYPTO 2016ved under the framework of primal-dual algorithms. Experimental evaluation shows that the proposed method can significantly improve the restoration quality of the images, compared to the existing techniques.作者: Axillary 時間: 2025-3-25 08:54
,Hiding Video in?Images: Harnessing Adversarial Learning on?Deep 3D-Spatio-Temporal Convolutional Nege. (2) An extractor that reverse-engineers the embedder function to extract the hidden data inside the encoded image. A multi-discriminator GAN framework with multi-objective training for multimedia hiding is one of the novel contributions of this work.作者: 并置 時間: 2025-3-25 15:30
,A Bayesian Approach to?Gaussian-Impulse Noise Removal Using Hessian Norm Regularization,ved under the framework of primal-dual algorithms. Experimental evaluation shows that the proposed method can significantly improve the restoration quality of the images, compared to the existing techniques.作者: 委托 時間: 2025-3-25 19:06
,Left Ventricle Segmentation of?2D Echocardiography Using Deep Learning,his paper. On the CAMUS dataset, the Vgg16 Unet model is new, and it has demonstrated promising results for endocardium segmentation. The dice metric values achieved for endocardium, epicardium, and left atrium are ., ., and . respectively.作者: Ballerina 時間: 2025-3-25 22:06
Conference proceedings 2023, CVIP 2022, held in Nagpur, India, November 4–6, 2022...The 110 full papers and 11 short papers?were carefully reviewed and selected from 307 submissions. Out of 121 papers, 109 papers are included in this book. The topical scope of the two-volume set focuses on Medical?Image? Analysis,? Image/? Vi作者: orthopedist 時間: 2025-3-26 03:40 作者: BUMP 時間: 2025-3-26 05:40 作者: VEIL 時間: 2025-3-26 09:21 作者: affinity 時間: 2025-3-26 15:09 作者: 附錄 時間: 2025-3-26 20:46
https://doi.org/10.1007/978-3-662-53018-4owards 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.作者: 即席 時間: 2025-3-26 22:49 作者: breadth 時間: 2025-3-27 02:18 作者: 最高點 時間: 2025-3-27 06:27
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.作者: 睨視 時間: 2025-3-27 11:59 作者: Facet-Joints 時間: 2025-3-27 16:29
,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.作者: cancellous-bone 時間: 2025-3-27 19:28 作者: excrete 時間: 2025-3-27 23:26
,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.作者: 壟斷 時間: 2025-3-28 03:41
,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.作者: 無力更進 時間: 2025-3-28 09:10 作者: jaunty 時間: 2025-3-28 10:40
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.作者: Flinch 時間: 2025-3-28 15:06
,Data-Centric Approach to?SAR-Optical Image Translation,s able to effectively capture and translate features unique to different land surfaces and experiments conducted on randomised satellite image inputs demonstrate that our approach is viable in significantly outperforming other baselines.作者: 玩笑 時間: 2025-3-28 21:53
1865-0929 Processing, CVIP 2022, held in Nagpur, India, November 4–6, 2022...The 110 full papers and 11 short papers?were carefully reviewed and selected from 307 submissions. Out of 121 papers, 109 papers are included in this book. The topical scope of the two-volume set focuses on Medical?Image? Analysis,? 作者: enterprise 時間: 2025-3-29 02:32
,Anomaly Detection in?ATM Vestibules Using Three-Stream Deep Learning Approach,feeds. ATM vestibules are one of the critical places where such anomalies must be detected. The problem lies around how we represent a video and further perform analysis on it to predict an anomaly. Another problem is the unavailability of data for this task specific to the ATM vestibule. To tackle 作者: 充足 時間: 2025-3-29 04:26
MIS-Net: A Deep Residual Network Based on Memorised Pooling Indices for Medical Image Segmentation, than classification architectures and require roughly twice as many network parameters. This large number of network layers may result in vanishing gradient or redundant computation, increased computational complexity and more memory consumption. Therefore, it is essential to develop an efficient d作者: gruelling 時間: 2025-3-29 08:44
HD-VAE-GAN: Hiding Data with Variational Autoencoder Generative Adversarial Networks, an embedder network (to hide a message inside the container) and an extractor network(to extract the hidden message from the encoded image). In the proposed method, we employ the generative power of a variational autoencoder with adversarial training to embed images. At the extractor, a vanilla con作者: intercede 時間: 2025-3-29 15:02 作者: Constrain 時間: 2025-3-29 16:48
,Hiding Video in?Images: Harnessing Adversarial Learning on?Deep 3D-Spatio-Temporal Convolutional Nes is a relatively new topic and has never been attempted earlier to our best knowledge. We propose two adversarial models that hide video data inside images: a base model with Recurrent Neural Networks and a novel model with 3D-spatiotemporal Convolutional Neural Networks. Both the models have two d作者: Cabinet 時間: 2025-3-29 22:03 作者: 教唆 時間: 2025-3-30 00:44 作者: 責難 時間: 2025-3-30 05:34 作者: 賞心悅目 時間: 2025-3-30 11:56
,A Segmentation Based Robust Fractional Variational Model for?Motion Estimation,, the presented model provides a generalization of integer order derivative based variational functionals and offers an enhanced robustness against outliers while preserving the discontinuity in the dense flow field. The motion is estimated in the form of optical flow. For this purpose, a level set 作者: Infiltrate 時間: 2025-3-30 12:37 作者: CRASS 時間: 2025-3-30 18:05
,CT Image Synthesis from?MR Image Using Edge-Aware Generative Adversarial Network,omy and the human body’s physiological processes. Radiotherapy planning requires the use of CT as well as MR images. The high radiation exposure of CT and the cost of acquiring multiple modalities motivate a reliable MRI-to-CT synthesis. The MRI-to-CT synthesiser introduced in this paper implements 作者: 拱墻 時間: 2025-3-30 23:34
Modified Scaled-YOLOv4: Soccer Player and Ball Detection for Real Time Implementation,ootball, and the passage of the football from one player to another, as well as significant interference between the football and players. Moreover, the diminished pixel resolution of players that are farther away and smaller in the frame in football matches, and the high velocity of the ball, will 作者: 轉(zhuǎn)折點 時間: 2025-3-31 01:51
,CandidNet: A Novel Framework for?Candid Moments Detection,atural head pose, body pose, eye gaze, facial expressions, human-object interactions, and background understanding is not a trivial task. It requires the timing and intuition of a professional photographer to capture fleeting moments. We propose a novel and real-time framework for detecting a candid作者: bypass 時間: 2025-3-31 07:22 作者: anaphylaxis 時間: 2025-3-31 12:56
Single Image Dehazing Using Multipath Networks Based on Chain of U-Nets,ed outdoors are seriously degraded in color and contrast due to extreme weather conditions like fog, haze, snow and rain. This poor visual quality can inhibit the performance, which is intended to operate on clear conditions like object detection. In recent years, single-image dehazing, which recove作者: Solace 時間: 2025-3-31 14:06 作者: spondylosis 時間: 2025-3-31 20:52 作者: 朋黨派系 時間: 2025-4-1 01:41 作者: thrombus 時間: 2025-4-1 05:47 作者: PLIC 時間: 2025-4-1 07:10 作者: Sinus-Rhythm 時間: 2025-4-1 11:27 作者: conservative 時間: 2025-4-1 17:30 作者: 使閉塞 時間: 2025-4-1 22:33
Advances in Cryptology – ASIACRYPT 2023 than classification architectures and require roughly twice as many network parameters. This large number of network layers may result in vanishing gradient or redundant computation, increased computational complexity and more memory consumption. Therefore, it is essential to develop an efficient d作者: Additive 時間: 2025-4-2 00:35
Exact Security Analysis of?ASCON an embedder network (to hide a message inside the container) and an extractor network(to extract the hidden message from the encoded image). In the proposed method, we employ the generative power of a variational autoencoder with adversarial training to embed images. At the extractor, a vanilla con作者: 仔細檢查 時間: 2025-4-2 04:26
https://doi.org/10.1007/978-3-662-53018-4xplore the application of data hiding to analyse the model’s performance. Share-GAN consists of an embedder network (to encode secret messages into a cover), a U-Net autoencoder (that consists of encoder and decoder). The embedder’s encoder network is custom trained to act as an extractor network (t
