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Multi-Exposure Fusion Light Field image Quality Assessment Based on Object Detection

Multi-Exposure Fusion Light Field Image Quality Assessment B...

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IEEE International conference on Signal processing, communications and Computing (ICSPCC)

作者： Keke Yao Guanglong Liao Gangyi Jiang Yeyao Chen Mei Yu Faculty of Information Science and Engineering Ningbo University Ningbo China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

The multi-exposure fusion algorithms can effectively improve the dynamic range of light field imaging, but distortion is inevitably introduced during the process. Therefore, it is crucial to construct an effective quality assessment method for multi-exposure fusion light field images (MEF-LFIs). Aiming at the significant artifact distortion phenomenon of MEF-LFIs and the limitations of the traditional dynamic region extraction methods, the MEF-LFI quality assessment method combined with object detection is proposed. Firstly, an object detection model is utilized to extract dynamic region masks of the original LFI sequences, and the artifact feature fusion module is constructed for artifact distortion. Subsequently, the MEF-LFIs are converted from the RGB color space to the HSV color space to measure color distortion. Then, the multi-stream feature extraction networks with attention mechanism are constructed to extract artifact, spatial and angular distortion features, respectively. Finally, a regression model is constructed to measure the visual quality of MEF-LFIs. Experimental results show that the proposed method outperforms many representative image quality assessment methods and maintains well consistency with the human visual perception.

关键词： image quality Attention mechanisms image color analysis Heuristic algorithms Object detection Feature extraction Distortion Light fields Quality assessment Distortion measurement

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722837

The proceedings contain 46 papers. The special focus in this conference is on Science, Engineering Management and Information Technology. The topics include: Verification and Validation of Knowledge Engineering Systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing Machine Learning Methods for Accurate Detection;monkeypox Detection with K-mer Using Machine Learning Algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction;image processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest Software;a Diagnosis Model Based on Federated Learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces images Using Transfer Learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep Learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of Signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time Based on the Digital Twin;role of Top Management Commitment and Information Technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722868

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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

IEEE visual communications and image processing (VCIP)

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

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.

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A three-level QR code sharing scheme based on SLIC and Hamming code

A three-level QR code sharing scheme based on SLIC and Hammi...

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2022 IEEE International conference on Signal processing, communications and Computing, ICSPCC 2022

作者： Wu, Wei Zhang, Lina Zhang, Jiehui Cui, Chenyu Zhang, Xiaoyu Liu, Miao Xi'an University of Science and Technology College of Computer Science and Technology Xi'an China Shaanxi Normal University College of Computer Science Xi'an China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

QR code is a typical two-dimensional code tag consisting of square symbols, which is widely used in social life due to the advantages of high readability and large storage capacity. However, its security performance is low, which has caused widespread concern among scholars in various industries. This paper proposes a three-level QR code scheme based on superpixel segmentation in response. Public information can be scanned directly from shares using any standard QR code scanner. Moreover, secret information can only be obtained by the dissimilarity of shares. The reconstructed secret image in this paper retains the main features of the embedded image, and the scheme is resistant to deformation, high load, and robust because it retains the advantages of visual ciphers and QR codes. Experimental results and analysis show this scheme has many advantages over other schemes. © 2022 IEEE.

关键词： Resistance visualization Codes Signal processing algorithms Symbols QR codes Feature extraction

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Retinal image Analysis Approach for Diabetic Retinopathy Grading 1

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15th International conference on Pattern Recognition and Information processing, PRIP 2021

作者： Golub, Yuliya Lukashevich, Marina Starovoitov, Valery Joint Institute of Informatics Problems The National Academy of Science of Belarus Surganova Street 6 Minsk Belarus Belarusian State University of Informatics and Radioelectronics Platonova Street 39 Minsk Belarus

ISBN: (数字)9783030988838

ISBN: (纸本)9783030988821

The eye is a complex organ that performs many functions. One of its components is the retina. One of the important properties of the retina is the reflection of the visible signs of a number of diseases. Diabetic retinopathy is the most common complication and the main cause of vision loss in diabetic patients. The retina also shows signs of other diseases that strongly affect visual impairment, such as cataracts, glaucoma, and macular degeneration. All of these diseases begin asymptomatically. Therefore, it is important to detect them at the very beginning of the disease and prescribe treatment. Advances in artificial intelligence (AI), especially in deep learning, are improving pathological image analysis in daily clinical practice. We have developed an approach for identifying and classifying diabetic retinopathy. It includes a universal method for segmentation of the retinal region for arbitrary fundus images and an assessment of the quality of the original image. Low quality images are excluded from further analysis. © 2022, Springer Nature Switzerland AG.

关键词： image analysis

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

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

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IEEE International conference on Signal processing, communications and Computing (ICSPCC)

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

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

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.

关键词： Deep learning visualization Accuracy Noise Predictive models Brain modeling Feature extraction Electroencephalography Vectors image reconstruction

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Hybrid Representation for 4D Medical image Compression

Hybrid Representation for 4D Medical Image Compression

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IEEE visual communications and image processing (VCIP)

作者： Wuyang Zheng Jiarui Meng Jiaqi Zhang Jian Zhang Siwei Ma School of Electronic and Computer Engineering Peking University China School of Computer Science Peking University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Due to the substantial storage requirements of the 4D medical images, achieving efficient compression of such images is a crucial topic. Existing traditional image/video coding methods have achieved remarkable results in most compression tasks, but their performance in encoding 4D medical images remain poor. This is because these methods cannot fully exploit the spatio-temporal correlations in 4D images. Recently, implicit neural representation (INR) based image/video compression methods have made significant progress, with coding performance comparable to traditional methods. However, they also suffer from significant performance losses in 4D medical image compression like traditional methods. In this paper, we propose an efficient hybrid representation framework, which includes six learnable feature planes and a tiny MLP decoder. This framework alleviates the issue of previous methods lacking the ability to utilize the spatio-temporal correlations in 4D medical images, enabling it to capture these information more effectively. We also introduce a novel adaptive plane scaling strategy that allocates the numbers of parameter in each plane based on the resolution of the image. This design allows the model to further enhance the reconstruction quality at the same compression ratio. Extensive experiments show that our model achieves better RD performance compared to traditional and INR-based methods, and it also offers faster encoding speeds than INR-based methods.

关键词： Adaptation models image coding Correlation image resolution visual communication Transforms Data models Decoding image reconstruction Biomedical imaging

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Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting

Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussi...

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IEEE visual communications and image processing (VCIP)

作者： Jiarui Meng Haijie Li Yanmin Wu Qiankun Gao Shuzhou Yang Jian Zhang Siwei Ma School of Electronic and Computer Engineering Peking University China School of Computer Science Peking University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

3D Gaussian Splatting (3DGS) has significantly advanced 3D scene reconstruction and novel view synthesis. However, like Neural Radiance Fields (NeRF), 3DGS struggles with accurately modeling physical reflections, particularly in mirrors, leading to incorrect reconstructions and inconsistent reflective properties. To address this challenge, we introduce Mirror-3DGS, a novel framework designed to accurately handle mirror geometries and reflections, thereby generating realistic mirror reflections. By incorporating mirror attributes into 3DGS and leveraging plane mirror imaging principles, Mirror-3DGS simulates a mirrored viewpoint from behind the mirror, enhancing the realism of scene renderings. Extensive evaluations on both synthetic and real-world scenes demonstrate that our method can render novel views with improved fidelity in real-time, surpassing the state-of-the-art Mirror-NeRF, especially in mirror regions.

关键词： Training Geometry Three-dimensional displays visual communication Rendering (computer graphics) Neural radiance field Reflection Real-time systems Mirrors image reconstruction

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Film Grain Regions characteristics SEI message

Film Grain Regions characteristics SEI message

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IEEE visual communications and image processing (VCIP)

作者： Philippe de Lagrange Gilles Teniou Edouard Francois Stephen Wenger Franck Galpin InterDigital Cesson-Sevigne France Tencent Cesson-Sevigne France Tencent America LLC Palo Alto CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The film grain characteristics (FGC) Supplemental Enhancement Information (SEI) message was developed in 2003 and was incorporated into the H.264/AVC specification then ported into the HEVC, VVC and Versatile supplemental enhancement information (VSEI) specifications. The FGC SEI message is used to signal and convey to video decoders metadata characterizing a film grain signal, that can be synthetized and superposed to the decoded video as a post-decoding process. It is an efficient solution to mimic the native film grain present in the input video that may have been removed by the encoding, enabling artistic intent preservation. It is also considered as an efficient solution to bring perceptual quality improvements by mitigating visual artefacts resulting from the coding process. The FGC SEI message parameters relate to the entire picture and do not allow a fine local control of the film grain. This paper describes the Film Grain Regions characteristics (FGR) SEI message, submitted as a candidate new VSEI SEI message, that enhances the FGC SEI by adding region-based control parameters enabling to adjust the synthesized film grain per region. After introducing the syntax and semantics of the FGC SEI message, the paper describes the additional features brought by the FGR SEI message. The region-based grain adaptation is showcased on different use cases that illustrate the potential benefits of the technology.

关键词： visualization image coding visual communication Semantics Syntactics Metadata Decoding

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