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检索条件"任意字段=2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023"

共 2298 条记录，以下是241-250 订阅

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SALIENT OBJECT DETECTION IN image SEQUENCES VIA SPATIAL-TEMPORAL CUE

SALIENT OBJECT DETECTION IN IMAGE SEQUENCES VIA SPATIAL-TEMP...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Gan, Chuang Qin, Zengchang Xu, Jia Wan, Tao Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing 100084 Peoples R China Beihang Univ Intelligent Comp & Machine Learning Lab Beijing Peoples R China Beihang Univ Sch Biol Sci & Med Engn Beijing Peoples R China

ISBN: (纸本)9781479902880

Contemporary video search and categorization are non-trivial tasks due to the massively increasing amount and content variety of videos. We put forward the study of visual saliency models in video. Such a model is employed to identify salient objects from the image background. Starting from the observation that motion information in video often attracts more human attention compared to static images, we devise a region contrast based saliency detection model using spatial-temporal cues (RCST). We introduce and study four saliency principles to realize the RCST. This generalizes the previous static image for saliency computational model to video. We conduct experiments on a publicly available video segmentation database where our method significantly outperforms seven state-of-the-art methods with respect to PR curve, ROC curve and visual comparison.

关键词： object detection saliency spatial-temporal cue

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Low Light RAW image Enhancement Using Paired Fast Fourier Convolution and Transformer

Low Light RAW Image Enhancement Using Paired Fast Fourier Co...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Zhang, Yichi Liu, Hengyu Ding, Dandan Ma, Zhan Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781665475921

Compared to RGB images non-linearly mapped from RAW data through the image Signal Processor (ISP), RAW data are linear to scene radiance and contain more native information, which is better to be modeled in many vision tasks. This work proposes to enhance low-light images in the RAW domain via a cross-scale framework using paired Fast Fourier Convolution (FFC) and Transformer, driving the network to characterize images effectively. The entire framework has three scales to abstract low-level, mid-level, and high-level representations of input images. We embed paired FFC and Transformer in each scale to attain spatial-spectral information extraction and aggregation. Specifically, by transforming features from the spatial domain into the spectral domain with FFC, pixel correlations can be effectively exploited locally and globally, generating representative features for the input image. Immediately, the Transformer using multi-head self-attention mechanism is applied to aggregate and embed important features. Experimental results demonstrate that our method significantly outperforms state-of-the-art low-light enhancement works in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Meanwhile, the perceptual quality of the proposed method is more visually pleasing than that of other methods.

关键词： Computational photography low-light transformer fast Fourier convolution

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LOW COMPLEXITY image MATCHING USING COLOR BASED SIFT

LOW COMPLEXITY IMAGE MATCHING USING COLOR BASED SIFT

引用

ieee international conference on visual communications and image processing (vcip)

作者： Nagar, Abhishek Saxena, Ankur Bucak, Serhat Fernandes, Felix Bhat, Kong-Posh Samsung Telecommun Amer 1301 East Lookout Dr Richardson TX 75082 USA

ISBN: (纸本)9781479902880

image matching and search is gaining significant commercial importance nowadays due to various applications it enables such as augmented reality, image-queries for internet search, etc. Many researchers have effectively used color information in an image to improve its matching accuracy. These techniques, however, cannot be directly used for large scale mobile visual search applications that pose strict constraints on the size of the extracted features, computational resources and the system accuracy. To overcome this limitation, we propose a new and effective technique to incorporate color information that can use the SIFT extraction technique. We conduct our experiments on a large dataset containing around 33, 000 images that is currently being investigated in the MPEG-Compact Descriptors for visual Search Standard and show substantial improvement compared to baseline.

关键词： image retrieval keypoints color SIFT mobile visual search MPEG-CDVS

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A Novel Learnable Orthogonal Wavelet Unit Neural Network with Perfection Reconstruction Constraint Relaxation for image Classification

A Novel Learnable Orthogonal Wavelet Unit Neural Network wit...

引用

2023 ieee international conference on visual communications and image processing, vcip 2023

作者： Le, An D Jin, Shiwei Bae, You Suk Nguyen, Truong University of California San Diego Jacobs School of Engineering California United States Technology University of Korea Department of Computer Engineering Gyeonggi-do Korea Republic of

ISBN: (纸本)9798350359855

CNNs utilize lowpass frequency features, evidenced by max pooling operations that preserve dominant features. Addressing this, we used a one-layer FCN using both coarse and detailed components via DWT. We then developed UwU, a learnable wavelet-based unit that integrates the PR constraint relaxation, allowing feature map component fine-Tuning. Distinctively, UwU's coefficients are trainable, unlike prior studies. This is the first work that utilizes PR constraint relaxation to enhance CNNs. Our innovative techniques serve to enhance stride-convolution, pooling, and downsampling units in CNNs. We tested these improved units using the ResNet family architectures against traditional frequency and wavelet-based units. Performance metrics from CIFAR10, imageNet1K, and the DTD show promising results. Particularly, while CIFAR10 results are on par with other methods, there's a marked performance improvement on imageNet1K and DTD. Further, integrating UwU into the ResNet18-based encoder of the CFLOW-AD system yields competitive anomaly detection results, particularly for hazelnut images in the MVTecAD dataset. © 2023 ieee.

关键词： Wavelet transforms

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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...

引用

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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DISTINGUISHING COMPUTER-GENERATED imageS FROM PHOTOGRAPHIC imageS: A TEXTURE-AWARE DEEP LEARNING-BASED METHOD

DISTINGUISHING COMPUTER-GENERATED IMAGES FROM PHOTOGRAPHIC I...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Zhang, Zicheng Sun, Wei Min, Xiongkuo Wang, Tao Lu, Wei Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781665475921

With the rapid development of computer graphics and generative models, computers are capable of generating images containing non-existent objects and scenes. Moreover, the computer-generated (CG) images may be indistinguishable from photographic (PG) images due to the strong representation ability of neural network and huge advancement of 3D rendering technologies. The abuse of such CG images may bring potential risks for personal property and social stability. Therefore, in this paper, we propose a dual-stream neural network to extract features enhanced by texture information to deal with the CG and PG image classification task. First, the input images are first converted to texture maps using the rotation-invariant uniform local binary patterns. Then we employ an attention-based texture-aware feature enhancement module to fuse the features extracted from each stage of the dual-stream neural network. Finally, the features are pooled and regressed into the predicted results by fully connected layers. The experimental results show that the proposed method achieves the best performance among all three popular CG and PG classification databases. The ablation study and cross-database validation experiments further confirm the effectiveness and generalization ability of the proposed algorithm.

关键词： Computer-generated photographic image classification dual-stream neural network texture map

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Perceptual image Compression With Conditional Diffusion Transformers

Perceptual Image Compression With Conditional Diffusion Tran...

引用

2024 conference on visual communications and image processing

作者： Mao, Rui Feng, Xinmin Gao, Changsheng Li, Li Liu, Dong Sun, Xiaoyan Univ Sci & Technol China MOE Key Lab Brain Inspired Intelligent Percept & Hefei Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Generative models have significantly advanced generative AI, particularly in image and video generation. Recognizing their potential, researchers have begun exploring their application in image compression. However, existing methods face two primary challenges: limited performance improvement and high model complexity. In this paper, to address these two challenges, we propose a perceptual image compression solution by introducing a conditional diffusion model. Given that compression performance heavily depends on the decoder's generative capability, we base our decoder on the diffusion transformer architecture. To address the model complexity problem, we implement the diffusion transformer architecture with Swin transformer. Equipped with enhanced generative capability, we further augment the decoder with informative features using a multi-scale feature fusion module. Experimental results demonstrate that our approach surpasses existing perceptual image compression methods while achieving lower model complexity.

关键词： Diffusion transformer (DiT) image generation perceptual image compression Swin transformer

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Fast Retrieval of Pharmaceutical Packaging images Using Keypoint Matching with Angle and Scale Voting for Outlier Rejection

Fast Retrieval of Pharmaceutical Packaging Images Using Keyp...

引用

2024 conference on visual communications and image processing

作者： Zakaria, Yona Ishiyama, Rui Ishidera, Eiki Matsui, Tomokazu Yasumoto, Keiichi Nara Inst Sci & Technol Nara Japan Univ Dodoma Dodoma Tanzania NEC Corp Ltd Tokyo Japan RIKEN Ctr Adv Intelligence Project Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Counterfeit medicines present a severe public health threat, especially in low-resource countries where consumers lack reliable means to verify the medicines they purchase. visual inspection of medicine packaging images through keypoint matching techniques offers a promising approach for detecting design inconsistencies that could indicate counterfeit products. However, conventional methods often struggle with high computational costs and reduced accuracy when processing images of varying quality and perspectives. To address these limitations, we propose the Angle and Scale Voting (ASVote) method, which enhances keypoint-based image matching by introducing a 2D voting mechanism that leverages relative angles and scales of the keypoints to eliminate false matches(outliers) while identifying consistent matches (inliers). This approach significantly improves both processing time and accuracy. Experiments on a real-world dataset of medicine packages show that ASVote improves processing time and accuracy, outperforming conventional methods.

关键词： keypoint feature matching RANSAC USAC image retrieval visual inspection counterfeit drugs

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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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Learned image Compression with Quantization Error Compensator

Learned Image Compression with Quantization Error Compensato...

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2024 conference on visual communications and image processing

作者： Fu, Nianxiang Chen, Zhenzhong Wang, Huairui Liu, Shan Wuhan Univ Hubei Luojia Lab Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Shenzhen Peoples R China Tencent Amer Palo Alto CA USA

ISBN: (纸本)9798331529543;9798331529550

Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of quantization, inherent to lossy image compression, introduces errors that can degrade the quality of the reconstructed image. To address this challenge, we propose a novel Quantization Error Compensator (QEC), which leverages spatial context within latent representations and hyperprior information to effectively mitigate the impact of quantization error. Moreover, we propose a tailored quantization error optimization training strategy to further improve rate-distortion performance. Notably, QEC serves as a lightweight, plug-and-play module, offering high flexibility and seamless integration into various learned image compression methods. Extensive experimental results consistently demonstrate significant coding efficiency improvements achievable by incorporating the proposed QEC into state-of-the-art methods, with a slight increase in runtime.

关键词： Learned image compression quantization error compensator quantization error

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