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检索条件"任意字段=Conference on Visual Communications and Image Processing 2005"

共 4633 条记录，以下是181-190 订阅

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Cryptanalysis of a Reversible Data Hiding Scheme in Encrypted images by Improved Redundant Space Transfer

Cryptanalysis of a Reversible Data Hiding Scheme in Encrypte...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Qu, Lingfeng He, Hongjie Amirpour, Hadi Ghanbari, Mohammad Timmerer, Christian Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China Southwest Jiaotong University School of Information Science and Technology Chengdu China Alpen-Adria-Universit Christian Doppler Laboratory ATHENA Klagenfurt Austria

ISBN: (纸本)9798350359855

In this paper, we propose a novel attack model called the Got Plaintext Attack (GPA), where the attacker only requires one plaintext and the ciphertext image set stored in the cloud to attack the content of the ciphertext image. Using this model, we examine the security of the Improved Redundant Space Transfer (IRST) encryption method. To this end, we define an ordered characteristic matrix based on the properties of the three keys used in IRST. By comparing the histogram distance of the ordered characteristic matrix, we are able to obtain a plain-ciphertext pair. Furthermore, by leveraging the invariant properties of the ordered characteristic matrix of image blocks in the plain-ciphertext pair, we estimate the block permutation Π2 and the bit-plane permutation sequence Π1. Our experiments show that the accuracy of estimating Π2 is higher than 70% for block sizes of 3x3 pixels or larger. Despite a 40% accuracy in estimating Π1, the content information of the ciphertext image can still be exposed. © 2023 IEEE.

关键词： image enhancement

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A Noval image Sensing Bionic Pre-encoder

A Noval Image Sensing Bionic Pre-encoder

引用

International conference on communications, Signal processing, and Systems, CSPS 2023

作者： Li, Jindong Hu, Xiao Huang, Zishen Zhu, Mingjin School of Mechanical and Electrical Engineering Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819975396

image preprocessing is important for deep learning image recognition tasks, and this paper introduces a new method called a bionic precoder, inspired by the human retinal visual system, to improve deep learning image recognition models. The precoder simulates a non-uniform distribution of retinal photoreceptors and iterates with trainable parameters. The trained precoder performs retinal-like preprocessing to improve the quality of the input data. Significant improvements in the accuracy/recall curve and receiver operating characteristic curve were observed experimentally. This study presents a promising technique for incorporating precoders into deep learning, leading to more accurate and efficient image recognition models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bionics

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Multi-scale Parallax Attention for Stereo image Super-Resolution

Multi-scale Parallax Attention for Stereo Image Super-Resolu...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Zhao, Zilin Li, Sumei Gao, Zhen Tianjin University School of Electrical and Information Engineering Tianjin China

ISBN: (纸本)9798350359855

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the existing methods are unable to obtain rich cross-view information at a low computational cost. In addition, these methods treat each pixel equally when fusing the cross-view information with the intra-view information, resulting in non-robustness of the fused information. In this work, we propose a multi-scale parallax attention stereo super-resolution network (MPASSRnet) to address these problems. Firstly, we design a multi-scale parallax attention module (MSPAM), which computes the similarity between stereo images on multiple scale images based on the introduction of pixel-To-patch matching, thus acquiring multi-scale cross-view information at a low computational cost. Second, we propose a pixel attention fusion module (PAFM), which introduces spatial attention (SA) by calculating the correlation between cross-view information and intra-view information, and combines channel attention (CA) to make the fused information more robust. Finally, extensive experiments show that our method achieves state-of-The-Art performance on the benchmark datasets. © 2023 IEEE.

关键词： Pixels

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VCIP 2018 - IEEE International conference on visual communications and image processing

VCIP 2018 - IEEE International Conference on Visual Communic...

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33rd IEEE International conference on visual communications and image processing, VCIP 2018

ISBN: (纸本)9781538644584

The proceedings contain 125 papers. The topics discussed include: two-stream federated learning: reduce the communication costs;a new update strategy for blocks with low correlation in 3-D recursive search;eye movement pattern modeling and visual comfort viewing S3D images;motion trajectory based spatial-temporal degradation measurement for video quality assessment;two-pass rate control for constant quality in high efficiency video coding;adaptive motion vector prediction for omnidirectional video;generative adversarial network-based frame extrapolation for video coding;a CNN-based in-loop filter with CU classification for HEVC;synthesizing 3D acoustic-articulatory mapping trajectories: predicting articulatory movements by long-term recurrent convolutional neural network;analysis of smoothed LHE methods for processing images with optical illusions;and deep network with spatial and channel attention for person re-identification.

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image Forgery Detection Using Hybrid Model

Image Forgery Detection Using Hybrid Model

引用

2024 IEEE International conference on Intelligent Signal processing and Effective Communication Technologies, INSPECT 2024

作者： Metri, Priya Balla, Sakshi Pawar, Shreya Sharma, Atharva Sakunde, Prashant Dr. D. Y. Patil Institute of Technology Artificial Intelligence and Data Science Pimpri Pune India

ISBN: (纸本)9798350379525

The growing demand for trustworthy picture forgery detection techniques to maintain the integrity of visual content across a range of applications is addressed in this work. To improve picture authentication accuracy and efficiency, a user-friendly cascaded framework combining Support Vector Machines (SVM) and VGG 16 is presented. First, a highly effective supervised learning system called Support Vector Machines (SVM) rapidly filters photos by identifying overt alteration indi-cators. After passing this phase, images are examined and refined even more with the help of the VGG 16 deep learning model. When SVM and VGG16 are combined, their complimentary strengths are utilised, which improves accuracy and speeds up identification when compared to other approaches. According to benchmark datasets, the proposed method outperforms earlier methods with respect to accuracy and computational efficiency. This strategy may benefit many real-world applications- based on forensic verification, verification of media integrity, medical image authentication, and intellectual property protection-in the here-and-now solutions. This work enhances the confidence and trustworthiness of digital communications by providing an efficient, friendly user tool for the verification of visual content. © 2024 IEEE.

关键词： Support vector machines

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Flexible Coding Order for Learned image Compression

Flexible Coding Order for Learned Image Compression

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Li, Yuqi Zhang, Haotian Liu, Dong University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9798350359855

Learned image compression (LIC) methods have made significant advances in recent years. In LIC, entropy model is an essential component, which utilizes conditional information to predict the probability distribution over the latent space. In the entropy models, many context models follow a spatially autoregressive paradigm, which leads to sequential coding. The autoregressive coding order, however, may be neither optimal nor efficient. We conduct an elaborate study on coding orders in LIC entropy models, shedding light on the potential for improving compression performance by adapting the coding order. We present Mask Modeling Context Model (MMCM), a transformer-based context model designed with a Patch-restricted Iterative Mask Modeling (PIMM) training strategy. Through training to predict the probability distribution of randomly masked tokens, we are able to use a patch-level arbitrary coding order to encode/decode the latent space in a few iterative steps. Additionally, we employ offline and online rate-distortion optimization to adaptively select the appropriate coding order for each image. Our extensive experimental results demonstrate that the proposed method achieves a better rate-distortion performance than the autoregressive context model while requiring less compression complexity. © 2023 IEEE.

关键词： image compression

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Learned image Compression with Variable Neural Network Architecture

Learned Image Compression with Variable Neural Network Archi...

引用

2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Zeng, Ziyi Yu, Lu Zhejiang University Hangzhou China

ISBN: (纸本)9798350359855

With the development of neural networks, the coding efficiency of learned image compression methods gradually exceeds that of traditional image codecs that are carefully designed and optimized by experts. However, the deep image compression model with an invariable network structure and fixed network parameters can only cover a limited range of bit rates. Using the same network structure for different bit rate ranges leads to sub-optimal coding efficiency. Meanwhile, the more complex network structure and the increase of network parameters cause higher model complexity, which makes it difficult for network training and practical application. In this paper, a new perspective is proposed to solve the above challenges. The idea of variable network architecture is introduced to the field of learned image compression. We devise two strategies with variable network architecture, which improve the coding performance of the learned image compression model from the coding efficiency and complexity respectively. Compared to the image compression method of fixed network structure, our first approach attains an average BDPSNR of 0.771dB and an average BDBR of-14.876%. The second attains an average BDPSNR of 0.320dB and an average BDBR of-5.471%. These results demonstrate the effectiveness of variable network structure in improving coding efficiency and controlling model complexity. © 2023 IEEE.

关键词： Network architecture

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Mobile Captured Glass Board image Enhancement 1

引用

7th International conference on Computer Vision and image processing, CVIP 2022

作者： Mahesh, Boddapati Mondal, Ajoy Jawahar, C.V. Centre for Visual Information Technology International Institute of Information Technology Hyderabad India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Note-taking methods and devices have improved tremendously over the past few decades, and people are finding new ways to write notes and take photos. Automatic extraction, recognition, and retrieval are necessary to process the huge chunk of digitized document data. However, an important step in all of these pipelines is the preprocessing step, mainly image enhancement or clean-up, which enhances the text regions and suppresses the non-text regions. In this article, we look at the problem of image enhancement or clean-up on one such important class of images (i.e., mobile captured glass board images). We present a simple yet efficient algorithm using the concepts of classical image processing techniques to solve the problem, and the obtained results are promising in comparison to the Office Lens. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： glass board document images image binarization image enhancement specular highlight removal

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Design of 3D image visual Communication System Based on Computer image Technology 4th

Design of 3D Image Visual Communication System Based on Comp...

引用

4th International conference on Machine Learning, image processing, Network Security and Data Sciences (MIND)

作者： Gou, Shuangxiao Mei, Ling Chongqing Univ Arts & Sci Coll Fine Arts & Design Chongqing 402160 Peoples R China Chongqing Water Resources & Elect Engn Coll Large Data Inst Chongqing 402160 Peoples R China

ISBN: (纸本)9783031243660;9783031243677

Computer imaging technology is a kind of use of digital photography, using a computer as amedium to realize interactive communication and interaction between humans and machines through the collection and processing of images and the editing and storage of graphic information. The purpose of this paper to study the design of the 3D image visual communication system based on computer image technology is to improve the mastery of 3D image technology and design the visual communication system. This article mainly uses experimental and comparative methods to analyze the feature extraction situation of the 3D image visual communication system, and finds that the error of the improved RANSAC algorithm in image feature extraction is about 54%, while the unimproved algorithm and other algorithms The error is greater. This shows that the improved algorithm proposed in this paper is incomparable in the 3D image visual communication system.

关键词： Computer image technology 3-dimensional image visual communication System design

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Composable image Coding for Machine via Task-oriented Internal Adaptor and External Prior

Composable Image Coding for Machine via Task-oriented Intern...

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2023 IEEE International conference on visual communications and image processing, VCIP 2023

作者： Liu, Jinming Jin, Xin Feng, Ruoyu Chen, Zhibo Zeng, Wenjun Shanghai Jiao Tong University China Eastern Institute of Technology Ningbo China Ningbo Institute of Digital Twin China University of Science and Technology of China China

ISBN: (纸本)9798350359855

Traditional image coding standards are typically optimized with a focus on human perception, which conflicts with the fact that most of the images are now analyzed by machines. To enable a variety of downstream intelligent tasks, contemporary approaches either utilize traditional codecs for image compression which are then used for task analysis, or develop a unified feature compression paradigm with deep learning techniques. However, they might suffer from accumulative errors and poor compatibility/generalization due to the conflict between standardized codecs and diverse machine tasks. We argue that a favorable image coding for machine (ICM) framework should have highly efficient adaptation capability, and take the ultimate task goals into account. Oriented at this, we propose a composable ICM solution dubbed Com-ICM, which develops plug-And-play lightweight internal adaptors injected into the codec architecture for efficient task transfer, and leverages off-The-shelf (large) models to provide external prior information for further task-oriented semantics learning. The internal adaptors (from the architectural aspect) and external priors (from the precondition aspect) complement each other, resulting in a mutually beneficial effect. We evaluate Com-ICM on diverse vision benchmarks, including image classification, object detection, and semantic segmentation, demonstrating its effectiveness and superiority. We are also actively submitting Com-ICM as a technical proposal to the international organization for standardization. © 2023 IEEE.

关键词： image compression

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