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High-Fidelity Free-View Talking Head Synthesis for Low-Bandwidth Video conference

High-Fidelity Free-View Talking Head Synthesis for Low-Bandw...

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IEEE visual communications and image processing (VCIP)

作者： Zhiyu Zhang Anni Tang Chen Zhu Guo Lu Rong Xie Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

As video conferencing becomes an indispensable part of human’s daliy life, how to achieve a high-fidelity calling experience under low bandwidth has been a popular and challenging issue. Deep generative models have great potential in low-bandwidth facial video compression due to the excellent generation capability based on abridged information. Nevertheless, exsiting deep generation-based compression methods tend to handle motion information in pure 2D or pseudo 3D space, causing facial distortion when large head poses are encountered. In this paper, we propose a 3D-aware high-fidelity facial video conferencing system based on a parameterized NeRF-based face model. Through the compression of the parameterized face model and the transmisstion of extracted facial parameters, we implement high-fidelity talking head synthesis for video conferencing at an ultra-low bitrate. Additionally, the 3D perception capability of the system allows for viewpoint control over the head, achieving higher interactivity and practicability. Extensive experiments verify the effectiveness of the proposed 3D-aware high-fidelity free-view facial video conferencing system.

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Generative Representation and Discriminative Classification for Few-shot Open-set Object Detection

Generative Representation and Discriminative Classification ...

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IEEE visual communications and image processing (VCIP)

作者： Peixue Shen Ruoqi Li Yan Luo Yiru Zhao Chao Gao Chongyang Zhang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China China Pacific Insurance(Group) Co. Ltd. Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Open-Set Object Detection (OSOD) aims to train detectors on closed-set datasets to detect known objects and identify unknown objects in open-set conditions. Traditional discriminative classifier-based OSOD methods struggle to accurately learn the decision boundary between known and unknown classes, often resulting in the misclassification of unknown samples. In this work, we aim to combine generative representation with discriminative classification to alleviate the issue of misclassification by transforming known-unknown recognition into a binary classification problem. The proposed two-stage OSOD approach proceeds as follows: during the generative representation stage, we employ Class-Conditioned Normalizing Flow (CCNF) to establish distribution mapping for each known category; In the discriminative classification stage, by utilizing a small number of unknown class samples, semi-push-pull supervised learning and entropy contrast learning are used to separate known and unknown classes. Extensive experiments demonstrate that our method significantly enhances OSOD performance, evidenced by a 25.8%-28.6% reduction in the Wilderness Index and a decrease of 4391-8870 units in Absolute Open-Set Errors on the test set VOC-COCO-T1.

关键词： Accuracy visual communication image processing Supervised learning Object detection Detectors Contrastive learning Entropy Object recognition Indexes

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Micro-Spatial Attention with Sparse Constraint for Self-Supervised Learning for Oleaginous Yeast image Representation

Micro-Spatial Attention with Sparse Constraint for Self-Supe...

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IEEE visual communications and image processing (VCIP)

作者： Kazuma Ohtomo Yukina Kitahara Ryosuke Harakawa Akihiro Nakamura Yosuke Shida Wataru Ogasawara Masahiro Iwahashi Department of Information Science and Control Engineering Nagaoka University of Technology Department of Science of Technology Innovation Nagaoka University of Technology Department of Electrical Electronics and Information Engineering Nagaoka University of Technology Department of Bioengineering Nagaoka University of Technology

Feature representation of oleaginous yeast images is essential for non-invasively observing their cultivation status. However, conventional methods are difficult to capture the visual characteristics of oleaginous yeasts at the extremely local spatial regions. To tackle this problem, this paper proposes micro-spatial attention with sparse constraints for self-supervised learning. Specifically, we newly design a micro-spatial attention module to focus on the extremely local spatial regions for feature representation. Unlike the existing spatial attention module, which requires the guide information (e.g., labels and textual queries) to focus on specific regions, our proposed module employs a sparse constraint. By combining the sparse constraint with self-supervised learning, we can automatically focus on essential regions, which appear in extremely local regions. This improves the feature representation power for oleaginous yeast images with micro-spatial contexts. Experiments on real Lipomyces starkeyi datasets, a specific type of oleaginous yeasts, confirm the superiority of our method. Moreover, our method realized clustering of oleaginous yeast images, enabling us to non-invasively observe the cultivation trends over time.

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Quantum Fourier Transform in image processing 1

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3rd International conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2023

作者： Mukhamedieva, D.T. Sobirov, R.A. Turgunova, N.M. Samijonov, B.N. Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University 39 Kari Niyaziy Street Tashkent100000 Uzbekistan Sejong University 209 Neungdong-ro Gwangjin-gu Seoul Korea Republic of

ISBN: (数字)9783031603181

ISBN: (纸本)9783031603174

This paper presents an approach to apply quantum Fourier transform (QFT) to image processing using quantum computing. The use of quantum computing for image analysis and processing is becoming increasingly relevant in modern science and technology. A quantum QFT circuit is presented, implemented using the Qiskit framework, which is a tool for programming quantum computers. The paper presents the basic steps of QFT and their application to a state vector representing the pixel intensities of an image. The influence of quantum transformation on the image structure is studied and the results are presented in the form of graphs and visualizations. In addition, we have introduced QFT quantum circuit inference capabilities for a more visual representation of the algorithm. The results highlight the potential of quantum computing in the field of image processing and open new prospects for the use of quantum technologies in the field of computer vision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Circuit simulation

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ACIQA: A Dataset and Method for Assessing the Imaging Quality of Automotive Cameras

ACIQA: A Dataset and Method for Assessing the Imaging Qualit...

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IEEE visual communications and image processing (VCIP)

作者： Yijie Huang Haoyang Ni Kaiwei Zhang Ziheng Jia Fangfang Lu Xiongkuo Min Guangtao Zhai Shanghai University of Electric Power China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The imaging quality of automotive cameras is crucial in complex driving environments. Therefore, it is essential to conduct subjective experiments that can realistically reflect drivers’ evaluation of the imaging quality of automotive cameras in real traffic scenarios. To accurately assess the imaging quality of automotive cameras, this paper proposes a no-reference quality assessment method with quality scores that are highly consistent with human subjective perception. Initially, this study constructs a new image quality assessment dataset and then obtains the subjective scores of image quality through subjective experiments. The dataset is constructed by using a variety of realistic props to simulate scene elements that might be captured by an automotive camera and are captured using a wide range of cameras with different sensor types, lens focus, and viewing angles, resulting in a dataset of diverse images. The objective quality assessment method proposed in this paper consists of an object detection network and a multi-branch quality evaluation network. The object detection network is responsible for identifying and classifying scene elements, while the multi-branch quality evaluation network performs feature extraction and score regression on various types of elements to effectively evaluate the imaging quality of the automotive cameras. In the experiments, this no-reference quality assessment method is tested on our built dataset, and the results show that the proposed method exhibits the best performance compared with the state-of-the-art image quality assessment methods.

关键词： image quality visual communication Object detection Cameras Feature extraction Quality assessment Object recognition Automotive engineering Lenses

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SketchAnimator: Animate Sketch via Motion Customization of Text-to-Video Diffusion Models

SketchAnimator: Animate Sketch via Motion Customization of T...

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IEEE visual communications and image processing (VCIP)

作者： Ruolin Yang Da Li Honggang Zhang Yi-Zhe Song PRIS School of Artificial Intelligence Beijing University of Posts and Telecommunications China SketchX CVSSP University of Surrey United Kingdom

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Sketching is a uniquely human tool for expressing ideas and creativity. The animation of sketches infuses life into these static drawings, opening a new dimension for designers. Animating sketches is a time-consuming process that demands professional skills and extensive experience, often proving daunting for amateurs. In this paper, we propose a novel sketch animation model SketchAnimator, which enables adding creative motion to a given sketch, like "a jumping car". Namely, given an input sketch and a reference video, we divide the sketch animation into three stages: Appearance Learning, Motion Learning and Video Prior Distillation. In stages 1 and 2, we utilize LoRA to integrate sketch appearance information and motion dynamics from the reference video into the pre-trained T2V model. In the third stage, we utilize Score Distillation Sampling (SDS) to update the parameters of the Bézier curves in each sketch frame according to the acquired motion information. Consequently, our model produces a sketch video that not only retains the original appearance of the sketch but also mirrors the dynamic movements of the reference video. We compare our method with alternative approaches and demonstrate that it generates the desired sketch video under the challenge of one-shot motion customization.

关键词： visual communication image processing Dynamics Animation Diffusion models Mirrors Automobiles Text to video Creativity

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Development of image processing Techniques in Crack Detection and Analysis

Development of Image Processing Techniques in Crack Detectio...

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Electrical, Computer, communications and Mechatronics Engineering (ICECCME), International conference on

作者： Luqman Sulaiman Al Dhafari Aftab Afzal Raid Ibrahim Al Bahrani Yahyah Al Busaidi Akbar Sheikh-Akbari Mohammad Sayeed Hossain Dept. Aeronautical Engineering Military Technological College Muscat Sultanate of Oman School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom

Inspection of aircraft skin is required as per the Corrosion Prevention and Control Program (CPCP) to ensure aircraft structural integrity. Human visual inspection is the most widely used technique in aircraft surface inspection, according to the CPCP. Scheduled inspections and regular maintenance of an aircraft through conventional methods constitute tedious and lengthy procedures. Often the visual inspections lead to subjective judgement and do not constitute repeatability. Many automated vision-based aircraft skin inspection systems have been designed to provide a safe, quick, and accurate visual assessment over the past years. This paper presents a section of research investigating defect detecting and accurately locating the outer body of an aircraft using an Unmanned Aerial Vehicle (UAV) to capture images and digital image processing techniques to locate possible cracks. The inspection system is used to initially detect locations of cracks (defects) on an aircraft’s outer skin and the detected crack is further investigated using thermal and ultrasound imaging methods. The scope of this paper includes a review of the design and development of a series of advanced dedicated image processing algorithms suitable for applying digital image processing on images captured from the outer surface of a typical aircraft fuselage.

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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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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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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...

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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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