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2024 International conference on image processing

作者： Suliman, Wael Deriche, Mohamed Werghi, Naoufel Beghdadi, Azeddine Prince Mohammad Bin Fahd Univ Ctr Artificial Intelligence Khobar Saudi Arabia Ajman Univ Coll Engn & IT Ajman U Arab Emirates Khalifa Univ Dept EE & CS Abu Dhabi U Arab Emirates Univ Sorbonne Paris Nord Lab Traitement Info Paris France

ISBN: (纸本)9798350349405;9798350349399

Cameras are now available at extremely low prices due to ongoing advancements in image acquisition hardware. However, the quality of images can be compromised by various distortions that occur throughout the entire process, from acquisition to processing and delivery. Over the past few decades, researchers have primarily focused on developing algorithms to assess the quality of distorted images. Unfortunately, certain distortions can also result from enhancement processes, such as over-enhancement and color saturation. Although there are metrics available for measuring contrast levels in images, there is currently no standard metric for evaluating the extent and effects of contrast enhancement. In this paper, we propose a new framework that expands the evaluation of contrast levels to ranking contrast-enhanced images. Our technique involves extracting a new set of features that accurately describe the effects of contrast enhancement. Furthermore, we integrate additional statistical indicators, such as skewness and kurtosis, which describe the degree of visual satisfaction linked to human perception. These identified characteristics are subsequently use with a simple classification module to determine the rank order for a given collection of contrast enhanced images. The results show excellent accuracy in correct ranking which outperforms state-of-the-art by more than 15%.

关键词： Blind image Quality Assessment image Ranking Contrast Enhancement Classification

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Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement

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ieee Transactions on Multimedia 2024年 26卷 5657-5669页

作者： Jiang, Nanfeng Chen, Weiling Lin, Jielian Zhao, Tiesong Lin, Chia-Wen Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350116 China Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression Artifacts Removal (VCAR) aims at reconstructing a high-quality video from its corrupted version of compression. Generally, this task is considered as a vision-related instead of media-related problem. In vision-related research, the visual quality has been significantly improved while the computational complexity and bitrate issues are less considered. In this work, we review the performance constraints of video coding and transfer to evaluate the VCAR outputs. Based on the analyses, we propose a Spatial-Temporal Attention-Guided Enhancement Network (STAGE-Net). First, we employ dynamic filter processing, instead of conventional optical flow method, to reduce the computational cost of VCAR. Second, we introduce self-attention mechanism to design Sequential Residual Attention Blocks (SRABs) to improve visual quality of enhanced video frames with bitrate constraints. Both quantitative and qualitative experimental results have demonstrated the superiority of our proposed method, which achieves high visual qualities and low computational costs. © 2023 ieee.

关键词： Computational complexity

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3D SEMANTIC SCENE COMPLETION FROM A DEPTH MAP WITH UNSUPERVISED LEARNING FOR SEMANTICS PRIORITISATION 31

3D SEMANTIC SCENE COMPLETION FROM A DEPTH MAP WITH UNSUPERVI...

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2024 International conference on image processing

作者： Alawadh, Mona Niranjan, Mahesan Kim, Hansung Univ Southampton Southampton Hants England Imam Mohammad Ibn Saud Islam Univ Riyadh Saudi Arabia

ISBN: (纸本)9798350349405;9798350349399

The Semantic Scene Completion (SSC) problem entails generating a comprehensive 3D voxel representation of a scene from a partial view, while simultaneously predicting volumetric occupancy and object category. A significant challenge in SSC is evaluating occluded regions in 3D space and accurately predicting object categories within an imbalanced setting. In addressing this challenge, our study explores SSC literature and introduces a simple, innovative class-balancing re-weighting technique rooted in an unsupervised clustering, leading to balanced learning and generalised representation. This method modulates the penalty on dataset classes during the CNN learning, emphasizing infrequent classes while moderately de-prioritizing the dominant ones, combining the strengths of both re-sampling and cost-sensitive learning enhancing the performance for both scene completion and scene semantics tasks. Our design, which relies on a single depth input without any RGB information, has shown to significantly outperform comparable baseline models. Our results are also competitively matched with other multi-input methods.

关键词： 3D reconstruction Single depth image Semantic Scene Completion

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BI-DIRECTIONAL TRACKLET EMBEDDING FOR MULTI-OBJECT TRACKING 31

BI-DIRECTIONAL TRACKLET EMBEDDING FOR MULTI-OBJECT TRACKING

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2024 International conference on image processing

作者： Bilgi, H. Cagri Alatan, A. Aydin METU Dept Elect & Elect Engn Ankara Turkiye METU Ctr Image Anal OGAM Ankara Turkiye

ISBN: (纸本)9798350349405;9798350349399

The last decade has seen significant advancements in multi-object tracking, particularly with the emergence of deep learning based methods. However, many prior studies in online tracking have primarily focused on enhancing track management or extracting visual features, often leading to hybrid approaches with limited effectiveness, especially in scenarios with severe occlusions. Conversely, in offline tracking, there has been a lack of emphasis on robust motion cues. In response, this approach aims to present a novel solution for offline tracking by merging tracklets using some recent promising learning-based architectures. We leverage a jointly performing Transformer and Graph Neural Network (GNN) encoder to integrate both the individual motions of targets and their interactions in between. By enabling bi-directional information propagation between the Transformer and the GNN, proposed model allows motion modeling to depend on interactions, and conversely, interaction modeling to depend on the motion of each target. The proposed solution is an end-to-end trainable model that eliminates the requirement for any handcrafted short-term or long-term matching processes. This approach performs on par with state-of-the-art multi-object tracking algorithms, demonstrating its effectiveness and robustness.

关键词： multi-object tracking tracklet embedding transformer graph neural network hierarchical tracking

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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...

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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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Mapping EEG Signals to visual Stimuli: A Deep Learning Approach to Match vs Mismatch Classification 14

Mapping EEG Signals to Visual Stimuli: A Deep Learning Appro...

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14th ieee International conference on Signal processing, communications and Computing, ICSPCC 2024

作者： Yang, Yiqian Zhao, Zhengqiao Wang, Qian Yang, Yan Chen, Jingdong School of Marine Science and Technology Northwestern Polytechnical University Center of Intelligent Acoustics and Immersive Communications Shaanxi Provincial Key Laboratory of Artificial Intelligence Shaanxi Xi'an710072 China Hong Kong University of Science and Technology Hong Kong North Electro-Optic Science & Technology Defense Co. Ltd China

ISBN: (纸本)9798350366556

Existing approaches to modeling associations between visual stimuli and brain responses are facing difficulties in handling between-subject variance and model generalization. Inspired by the recent progress in modeling speech-brain response, we propose a 'match-vs-mismatch' deep learning model in this study to classify whether a video clip elicits neural responses in recorded EEG signals. Our model employs dilated convolutional neural networks and gated recurrent units to extract features from both EEG and video signals, enabling the learning of associations between visual content and corresponding neural recordings. We demonstrate that our proposed model achieves the highest accuracy on unseen subjects compared to other baseline models. Additionally, we assess inter-subject noise using a subject-level silhouette score in the embedding space, revealing that our model effectively mitigates inter-subject noise and significantly reduces the silhouette score. Furthermore, we investigate Grad-CAM activation scores, revealing that brain regions linked to language processing contribute most to model predictions, followed by regions associated with visual processing. These findings hold promise for advancing neural recording-based video reconstruction and related applications. © 2024 ieee.

关键词： Convolutional neural networks

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Machine Learning Approach for Automatic Weld Defect Detection Using Radiographic images 2

Machine Learning Approach for Automatic Weld Defect Detectio...

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and communications Systems, ICMACC 2024

作者： Edupuganti, Venkata Ramana Penekalapati, Sai Varun Namala, Kiran Kumar VNR Vignana Jyothi Institute of Engineering and Technology Mechanical Engineering Department Hyderabad India

ISBN: (纸本)9798350366570

Radiographic images of weld beads are important to inspect subsurface weld defects for safety assurance. The diversified problems associated with the human analysis of radiographic images of the weldments paved the way to the use of machine learning approach for the detection of subsurface defects. Automatic weld defect inspection involves image pre-processing, defect detection, and classification of defects. The techniques of digitization of radiographs, image pre-processing, defect segmentation and classification are summarized. Advanced methods for radiographic weld defect image classification using machine learning and challenges associated with the current technology are discussed for future improvement. © 2024 ieee.

关键词： image segmentation

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AUDIO-visual QUALITY ASSESSMENT FOR USER GENERATED CONTENT: DATABASE AND METHOD 30

AUDIO-VISUAL QUALITY ASSESSMENT FOR USER GENERATED CONTENT: ...

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30th ieee International conference on image processing (ICIP)

作者： Cao, Yuqin Min, Xiongkuo Sun, Wei Zhang, Xiaoping Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Informat Proc Shanghai Peoples R China Toronto Metropolitan Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781728198354

With the explosive increase of User Generated Content (UGC), UGC video quality assessment (VQA) becomes more and more important for improving users' Quality of Experience (QoE). However, most existing UGC VQA studies only focus on the visual distortions of videos, ignoring that the user's QoE also depends on the accompanying audio signals. In this paper, we conduct the first study to address the problem of UGC audio and video quality assessment (AVQA). Specifically, we construct the first UGC AVQA database named the SJTU-UAV database, which includes 520 in-the-wild UGC audio and video (A/V) sequences, and conduct a user study to obtain the mean opinion scores of the A/V sequences. The content of the SJTU-UAV database is then analyzed from both the audio and video aspects to show the database characteristics. We also design a family of AVQA models, which fuse the popular VQA methods and audio features via support vector regressor (SVR). We validate the effectiveness of the proposed models on the three databases. The experimental results show that with the help of audio signals, the VQA models can evaluate the perceptual quality more accurately. The database will be released to facilitate further research.

关键词： User-generated content audio-visual quality assessment multimodal fusion

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Generalizable Whole Slide image Classification with Fine-Grained visual-Semantic Interaction

Generalizable Whole Slide Image Classification with Fine-Gra...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Hao Chen, Ying Chen, Yifei Yu, Rongshan Yang, Wenxian Wang, Liansheng Ding, Bowen Han, Yuchen Xiamen Univ Sch Informat Xiamen Peoples R China Huawei Xiamen Peoples R China Aginome Sci Xiamen Peoples R China Shanghai Jiao Tong Univ Shanghai Chest Hosp Dept Pathol Sch Med Shanghai Peoples R China

ISBN: (纸本)9798350353006

Whole Slide image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coarse-grained pathogenetic descriptions for visual representation supervision, which are insufficient to capture the complex visual appearance of pathogenetic images, hindering the generalizability of models on diverse downstream tasks. Additionally, processing high-resolution WSIs can be computationally expensive. In this paper, we propose a novel "Fine-grained visual-Semantic Interaction" (FiVE) framework for WSI classification. It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics. Specifically, with meticulously designed queries, we start by utilizing a large language model to extract fine-grained pathological descriptions from various non-standardized raw reports. The output descriptions are then reconstructed into fine-grained labels used for training. By introducing a Task-specific Fine-grained Semantics (TFS) module, we enable prompts to capture crucial visual information in WSIs, which enhances representation learning and augments generalization capabilities significantly. Furthermore, given that pathological visual patterns are redundantly distributed across tissue slices, we sample a subset of visual instances during training. Our method demonstrates robust generalizability and strong transferability, dominantly outperforming the counterparts on the TCGA Lung Cancer dataset with at least 9.19% higher accuracy in few-shot experiments. The code is available at: https://***/ls1rius/WSI FiVE.

关键词： Fine-Grained Generalizable Vision-Language-Model visual-Semantic Whole Slide image

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Multi-scale Parallax Attention for Stereo image Super-Resolution

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

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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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