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检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

共 25558 条记录，以下是4701-4710 订阅

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PERCEPTNET: A HUMAN visual SYSTEM INSPIRED NEURAL NETWORK FOR ESTIMATING PERCEPTUAL DISTANCE

PERCEPTNET: A HUMAN VISUAL SYSTEM INSPIRED NEURAL NETWORK FO...

引用

ieee International conference on image processing (ICIP)

作者： Hepburn, Alexander Laparra, Valero Malo, Jesus McConville, Ryan Santos-Rodriguez, Raul Univ Bristol Dept Engn Math Bristol Avon England Univ Valencia Image Proc Lab Valencia Spain

ISBN: (纸本)9781728163956

Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was usually taken from the human visual perceptual system and how the system processes different perturbations in order to replicate to what extent it determines our ability to judge image quality. While recent works have presented deep neural networks trained to predict human perceptual quality, very few borrow any intuitions from the human visual system. To address this, we present PerceptNet, a convolutional neural network where the architecture has been chosen to reflect the structure and various stages in the human visual system. We evaluate PerceptNet on various traditional perception datasets and note strong performance on a number of them as compared with traditional image quality metrics. We also show that including a nonlinearity inspired by the human visual system in classical deep neural networks architectures can increase their ability to judge perceptual similarity. Compared to similar deep learning methods, the performance is similar, although our network has a number of parameters that is several orders of magnitude less.

关键词： perceptual distance human visual system neural networks

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Improved Color Balance and Fusion for Enhancement of Underwater image

Improved Color Balance and Fusion for Enhancement of Underwa...

引用

International conference on Computer and communications (ICCC)

作者： Juniie Chen Yaiuan Wu School of Computer science China West Normal University Nanchong China

In underwater photography, the absorption and scattering of light may probably cause low contrast, blurred images, and color cast. We present an effective method for improving the quality of underwater images that have been degraded by medium scattering and absorption. It combines color compensation with multi-scale image fusion. Color compensation consists of independently correcting the value of $\mathbf{R}$ channel and G-B channel of the input image and adjusting the white balance of the corrected image. After color compensation, the color of degraded image is restored effectively, but the blurring of image edges and details receiving scattering effects cannot be remedied. This problem can be solved by multi-scale image fusion. In our method, we use Gaussian pyramid and Laplace pyramid images for fusion, and these two feature pyramids are constructed by using the image after the illumination adjustment. The experimental results illustrate that dark-area sensitivity, global contrast, and edge sharpness of images have been improved by our methods. Moreover, in terms of visual effects and assessment metrics of underwater images, our method excels over other methods.

关键词： Photography Sensitivity image color analysis Absorption image edge detection Scattering Lighting

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Innovative Solutions for Cervical Cancer: Deep Learning Models for Accurate Stage Prediction

Innovative Solutions for Cervical Cancer: Deep Learning Mode...

引用

Contemporary Computing and communications (InC4), ieee International conference on

作者： Roshan Kumar Yadav Divyam Kumar Kalyani Kadam Seeta Devi Rupali Gangarde Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Symbiosis International University (SIU) Pune India Department of Obstetrics and Gynecological Nursing Symbiosis College of Nursing Symbiosis International University (SIU) Pune India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University (SIU) Pune India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Cervical cancer, caused by the skin infection of the Human papillomavirus (HPV), ranked as the fourth most prevalent cancer globally, has profound and sometimes fatal implications for women. With approximately 500,000 new cases reported annually, the disease significantly impacts women's overall well-being. Ongoing efforts by researchers focus on developing a more cost-effective solution than regular HPV testing to manage and control this widespread health concern. Our research included an innovative framework for identifying and classifying stages of cervical cancer by processing the images of the tumours. To optimize the performance of our model, we developed a unique approach for selecting the best image data points by calculating a score, which is defined as the product of focus, sharpness, and resolution. Additionally, our study distinguishes itself by integrating three diverse feature extraction techniques–Local Binary Pattern (LBP), ResNet, and VGGNet–an inclusive strategy not commonly observed in similar projects. We even added deep convolutional architectures and machine learning techniques, including Random Forest, SVM, Decision Tree, XGBoost, a basic Neural Network Classifier, and a Multi-layered Perceptron (MLP). This hybrid approach allowed for an intrinsic comparison, identifying the most effective strategy among these. In contrast to current state-of-the-art methods, our approach yielded around 75% accuracy and 81% AUC score, which is greater than the existing studies on such dataset.

关键词： Support vector machines Accuracy image resolution Neural networks Predictive models Skin Cervical cancer

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OccTransformers: Learning occupancy using attention 17

OccTransformers: Learning occupancy using attention

引用

17th ieee International conference on Intelligent Computer Communication and processing, ICCP 2021

作者： Maxim, Bogdan Nedevschi, Sergiu Technical University of Cluj-Napoca Computer Science Departament Cluj-Napoca Romania

ISBN: (纸本)9781665409766

Recent years have seen significant progress in learning based approaches for 3D reconstruction. While different kind of representations have been tried, signed distance fields start to gain interest as they are easier to train, do not leave such a big memory footprint and yield competitive results, often beyond state-of-the-art. Self attention based Transformer models achieve state-of-the-art in different fields like sentence translation, image recognition and even multi-dimensional object classification by reformulating the problems as a sequence-to-sequence prediction. In this work we show that a transformer based architecture achieves state-of-the-art results even in the field of 3D re-construction from different inputs(single image, point cloud) without the need of specialized encoders or additional fine-tuning. Our architecture is trained separately for the tasks of noisy point cloud shape completion and for single image 3D reconstruction on the popular ShapeNet dataset, achieving state-of-the-art by a significant margin on the Chamfer distance metric and competitive results on IoU and Normal Consistency. We belive that transformer based architectures will play a significant role in the following years in the field of 3D reconstruction. © 2021 ieee.

关键词： image recognition

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Deep Residual Attention Network for Hyperspectral image Reconstruction 25

Deep Residual Attention Network for Hyperspectral Image Reco...

引用

25th International conference on Pattern Recognition (ICPR)

作者： Kohei, Yorimoto Han, Xian-Hua Yamaguchi Univ Grad Sch Sci & Technol Innovat Yamaguchi Japan

ISBN: (纸本)9781728188089

Coded aperture snapshot spectral imaging (CASSI) captures a full frame spectral image as a single compressive image and is mandatory to reconstruct the underlying hyperspectral image (HSI) from the snapshot as the post-processing, which is a challenge inverse problem due to its ill-posed nature. Existing methods for HSI reconstruction from a snapshot usually employs optimization for solving the formulated i mage degradation model regularized with the empirically designed priors, and still cannot achieve enough reconstruction accuracy for real HS image analysis systems. Motivated by the recent advances of deep learning for different inverse problems, deep learning based HSI reconstruction method has attracted a lot of attention and can boost the reconstruction performance. This study proposes a novel deep convolutional neural network (DCNN) based framework for effectively learning the spatial structure and spectral attribute in the underlying HSI with the reciprocal spatial and spectral modules. Further, to adaptively leverage the useful learned feature for better HSI i mage reconstruction, we integrate residual attention modules into our DCNN via exploring both spatial and spectral attention maps. Experimental results on two benchmark HSI datasets show that our method outperforms state-of-the-art methods in both quantitative values and visual effects.

关键词： image reconstruction

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History-Based MVP (HMVP) for Gradual Decoding Refresh of VVC

History-Based MVP (HMVP) for Gradual Decoding Refresh of VVC

引用

ieee visual communications and image processing (VCIP)

作者： Limin Wang Seungwook Hong Krit Panusopone Multimedia Technologies Research Nokia San Diego USA

ISBN: (纸本)9781728173221

Versatile Video Coding (VVC) is a new international video coding standard. One of the functionalities that VVC supports is so called Gradual Decoding Refresh (GDR). GDR is mainly for (ultra) low-delay applications. As the latest video coding standard, VVC employs many new and advanced coding tools. Among them is HMVP (History-based Motion Vector Prediction), which however can cause leaks for GDR applications. This paper analyzes the leak problem associated with HMVP for GDR and proposes suggestions on how to use HMVP for GDR applications.

关键词： Video coding image coding visual communication Decoding Standards

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The design of city brand visual image recognition system 2

The design of city brand visual image recognition system

引用

2nd ieee Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Wu, Yi Shen, Mingyuan Seoul National University College of Fine Arts Department of Crafts and Design Seoul08826 Korea Republic of Communication University of Shanxi School of Cultural Creativity and Management Jinzhong Shanxi030619 China

ISBN: (纸本)9781728190181

Thanks to the city brand strategy, more and more countries and cities carry out self marketing. In this regard, branding urban space is a key subdivision to achieve a place's publicity. Therefore, from the perspective of urban design, this paper discusses the factors and indicators affecting the branding of urban conventional space. From the perspective of city brand, through the research of relevant resources, we find out the most important factors that affect the brand of urban space, and give a table including the influencing factors and indicators of brand urban space. The results are a basic chart that can be used to compare urban space with major criteria. The conclusion reveals a fact: compared with other brand urban space, how many factors are included in a selected urban space. Regional cultural differences directly lead to the characteristics of urban development. The unique regional culture is the precious wealth of city brand image design, which has practical significance for creating characteristic city brand. From the perspective of regional culture, this paper discusses the positioning of city brand image, urban CI design, city brand image communication and management, hoping to promote urban development with the influence of city brand. The design of city brand image should not blindly 'create' personality, but should follow the law of regional culture development and 'seek roots' from the deep structure of regional culture and obtain enlightenment. This is the only way to shape the city brand image. © 2021 ieee.

关键词： Marketing

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Skin Cancer Diagnosis Using image processing and Xception-CNN Model

Skin Cancer Diagnosis Using Image Processing and Xception-CN...

引用

Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： Sunitha R Pankaja R Pavithra H C Abhilasha P Kumar Mohanesh B M Department of AI & ML B N M Institute of Technology Bangalore India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Skin cancer is recognized as one of the most lethal forms of cancer, often resulting in death if not detected early. Its aggressive tendency to spread to other parts of the body makes treatment in later stages particularly difficult, emphasizing the crucial need for early detection. As the incidence of skin cancer continues to rise, along with its associated high mortality rates and treatment expenses, there is an urgent demand for more effective early detection methods. Leveraging deep learning algorithms through a model-driven architecture has emerged as a promising solution for improving skin cancer diagnosis. In this work, present the development and implementation of a deep learning model designed to accurately classify images of skin lesions. The proposed approach uses the Xception model, an advanced convolutional neural network (CNN) architecture. Through training, the Xception model identifies patterns and features in skin lesion images that indicate whether the condition is benign or malignant. Proposed model achieved an accuracy rate of 86% in the classification task.

关键词： Photography Deep learning Wireless communication Training Computer architecture Skin Classification algorithms Lesions Convolutional neural networks Skin cancer

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Towards Universal GAN image Detection

Towards Universal GAN Image Detection

引用

ieee visual communications and image processing (VCIP)

作者： Davide Cozzolino Diego Gragnaniello Giovanni Poggi Luisa Verdoliva University Federico II of Naples Italy

ISBN: (纸本)9781728173221

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.

关键词： Training image coding visual communication Forensics Detectors Robustness

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A new deep fake method based on background removal 7

A new deep fake method based on background removal

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7th ieee International conference on Information Technology and Nanotechnology, ITNT 2021

作者： Kuznetsov, Andrey Samara National Research University Image Processing Systems Institute of RAS Branch of the FSRC 'Crystallography and Photonics' RAS Samara Russia

ISBN: (纸本)9781665432177

The modern digital space is saturated with a huge amount of data in the form of images and videos every day. All information contained is important to users, organizations and other consumers. It should be noted that the speed of information dissemination is so high that sometimes, in order to trace its original source, it is necessary to spend large computing resources. Moreover, during the search for the original source, information can be distorted beyond recognition and acquire new properties in the form of details and additional links. Attackers change information, as a rule, in order to compromise an individual, incite conflicts, undermine the reputation of individuals and legal entities, etc. Among the most modern and popular means is DeepFake technology, with the help of which a person's face can be replaced on an image or video. The paper proposes a new method for generating distorted images, based on removing the background in the image, to improve the visual quality of distortion. © 2021 ieee.

关键词： Generative adversarial networks

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