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IEEE Conference on Decision and Control

作者： Dong Yue Engang Tian Qing-Long Han Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Hubei 430074 China School of Electrical and Automation Engineering Nanjing Normal University 78 Bancang Street Nanjing 210042 China Center for Intelligent and Networked Systems and the School of Information and Communication Technology Central Queensland University Rockhampton Qld 4702 Australia

ISBN: (纸本)9781612848006

The event-triggered H_(infinity) control design is investigated for networked control systems with uncertainties and transmission delays. A novel event-triggering scheme is proposed, which has some advantages over traditional ones with a continuous detector. Considering the effect of the transmission delay, a delay system model for the analysis is firstly constructed. Then, based on the model and Lyapunov functional method, criteria for the stability with an H_(infinity) norm bound and criteria for the co-design of both the feedback gain and the trigger parameters are derived. In order to solve the feedback gain and the trigger parameters, the linear matrix inequality technique is employed. From the simulation example, it can be concluded that the proposed event-triggering scheme is superior to some other event-triggering schemes in some existing literature.

关键词： feedback gain Networked control systems Event trigger Delay systems Scheme Co-design control systems design control

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Learning Based Estimation of Video Coding Distortion

Learning Based Estimation of Video Coding Distortion

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Xiangwen Wang Zhengyi Luo Pengbo Li Li Song College of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China Shanghai University of Electric Power Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University 2 MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Coding distortion is a critical factor in video coding algorithms such as rate distortion optimization, rate control and optimal quantization. Accurate distortion estimation without complex pre-coding has always been highly desired. Traditional distortion estimation methods depend roughly on two assumptions—accurate modeling of the DCT coefficients and fine quantization. Unfortunately, these two assumptions may not be satisfied in most video coding situations. This paper proposes two new methods to estimate video coding distortion dropping the above assumptions. The first one is based on function fitting, modeling coding distortion as a function of the ratio of quantization step to the standard deviation of the residual image. The second one is machine learning based, where an ensemble learning model is employed and the feature vector is composed of the distortion of referenced frames, the variance of the residual image and the quantization step. The HEVC reference encoder HM with the hierarchical coding structure is employed for accuracy evaluation. The experimental results show the accuracy of the proposed methods is much higher than that of traditional ones.

关键词： Distortion Quantization (signal) Estimation Discrete cosine transforms Encoding Laplace equations Video coding

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Thqa: A Perceptual Quality Assessment Database for Talking Heads

Thqa: A Perceptual Quality Assessment Database for Talking H...

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IEEE International Conference on image processing

作者： Yingjie Zhou Zicheng Zhang Wei Sun Xiaohong Liu Xiongkuo Min Zhihua Wang Xiao-Ping Zhang Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China PengCheng Laboratory China Guangdong Lab of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Tsinghua Shenzhen International Graduate School Tsinghua University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In the realm of media technology, digital humans have gained prominence due to rapid advancements in computer technology. However, the manual modeling and control required for the majority of digital humans pose significant obstacles to efficient development. The speech-driven methods offer a novel avenue for manipulating the mouth shape and expressions of digital humans. Despite the proliferation of driving methods, the quality of many generated talking head (TH) videos remains a concern, impacting user visual experiences. To tackle this issue, this paper introduces the Talking Head Quality Assessment (THQA) database, featuring 800 TH videos generated through 8 diverse speechdriven methods. Extensive experiments affirm the THQA database’s richness in character and speech features. Subsequent subjective quality assessment experiments analyze correlations between scoring results and speech-driven methods, ages, and genders. In addition, experimental results show that mainstream image and video quality assessment methods have limitations for the THQA database, underscoring the imperative for further research to enhance TH video quality assessment. The THQA database is publicly accessible at https://***/zyj-2000/THQA.

关键词： Visualization Databases Shape image processing Mouth Manuals Media

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A Parallel-based Lifting Algorithm and VLSI Architecture for DWT

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Journal of Electronics(China) 2006年第2期23卷 244-248页

作者： Xiong Chengyi Tian Jinwen Liu Jian Gao Zhirong The State Key Lab of Education Commission for Image Processing and Intelligent Control Institute of Pattern Recognition ＆ Artificial Intelligence Huazhong Univ. of Science ＆ Tech. Wuhan 430074 China College of Electronic Info. Eng. South- Center Univ. for Nationalities Wuhan 430074 China Dept of Computer Science Wuhan Univ. of Science ＆ Eng. Wuhan 430074 China

A novel Parallel-Based Lifting Algorithm （PBLA） for Discrete Wavelet Transform （DWT）, exploiting the parallelism of arithmetic operations in all lifting steps, is proposed in this paper. It leads to reduce the critical path latency of computation, and to reduce the complexity of hardware implementation as well. The detailed derivation on the proposed algorithm, as well as the resulting Very Large Scale Integration （VLSI） architecture, is introduced, taking the 9/7 DWT as an example but without loss of generality. In comparison with the Conventional Lifting Algorithm Based Implementation （ClabI）, the critical path latency of the proposed architecture is reduced by more than half from （4Tm ＋ 8Ta）to Tm ＋ 4Ta, and is competitive to that of Convolution-Based Implementation （CBI）, but the new implementation will save significantly in hardware. The experimental results demonstrate that the proposed architecture has good performance in both increasing working frequency and reducing area.

关键词： Discrete Wavelet Transform (DFT) Lifting scheme Parallel Very Large Scale Integration(VLSI)

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Few-Shot Class-Incremental Learning with Prior Knowledge

arXiv

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arXiv 2024年

作者： Jiang, Wenhao Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Kargobot of DiDi Shanghai201210 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequently underestimated in these studies. Therefore, to enhance the generalization ability of the pre-trained model, we propose Learning with Prior Knowledge (LwPK) by introducing nearly free prior knowledge from a few unlabeled data of subsequent incremental classes. We cluster unlabeled incremental class samples to produce pseudo-labels, then jointly train these with labeled base class samples, effectively allocating embedding space for both old and new class data. Experimental results indicate that LwPK effectively enhances the model resilience against catastrophic forgetting, with theoretical analysis based on empirical risk minimization and class distance measurement corroborating its operational principles. The source code of LwPK is publicly available at: https://***/StevenJ308/LwPK. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning

arXiv

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arXiv 2024年

作者： Yang, Kuo Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The Kargobot of DiDi Shanghai201210 China The Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China The Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

For semi-supervised learning with imbalance classes, the long-tailed distribution of data will increase the model prediction bias toward dominant classes, undermining performance on less frequent classes. Existing methods also face challenges in ensuring the selection of sufficiently reliable pseudo-labels for model training and there is a lack of mechanisms to adjust the selection of more reliable pseudo-labels based on different training stages. To mitigate this issue, we introduce uncertainty into the modeling process for pseudo-label sampling, taking into account that the model performance on the tailed classes varies over different training stages. For example, at the early stage of model training, the limited predictive accuracy of model results in a higher rate of uncertain pseudo-labels. To counter this, we propose an Uncertainty-Aware Dynamic Threshold Selection (UDTS) approach. This approach allows the model to perceive the uncertainty of pseudo-labels at different training stages, thereby adaptively adjusting the selection thresholds for different classes. Compared to other methods such as the baseline method FixMatch, UDTS achieves an increase in accuracy of at least approximately 5.26%, 1.75%, 9.96%, and 1.28% on the natural scene image datasets CIFAR10-LT, CIFAR100-LT, STL-10-LT, and the medical image dataset TissueMNIST, respectively. The source code of UDTS is publicly available at: https://***/yangk/UDTS. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Low-Cost and Portable Physiological Signal Monitor Using Physrate Model

SSRN

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SSRN 2023年

作者： Tan, Xudong Zhu, Yan Cheng, Zenghui Hu, Menghan Zhang, Xinxin Pei, Gan Yu, Chao Li, Qingli Li, Wenfang Wang, Jia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai200025 China Department of Emergency and Critical Care Shanghai Changzheng Hospital Shanghai200003 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China

In this work, we propose a lightweight non-contact physiological signal monitor. The proposed system is equipped with the modified DeepPhys model and the rate estimation model PhysRate that enable non-contact physiological signal measurements without markers using the low-cost color camera. We introduce the constraint term guided by scene information into the original DeepPhys model to mitigate the overfitting problem of the original DeepPhys model. The results of validation experiments in ICU observation ward show that the modified DeepPhys model can effectively counteract the overfitting problem of the original DeepPhys model. The experimental results of PhysRate indicate that it exhibits certain performance improvements compared to other rate prediction models. In terms of system integration, the proposed physiological signal detection monitor includes Raspberry Pi 4B, RGB camera, 4G module, mobile power, and mobile terminal. With the help of above five modules, the system can analyze the captured signals at the edge and then transmit the processed data to the cloud via 4G network. The edge side is equipped with physiological signal detection model and the other data processing algorithms. On mobile devices, the users can view the relevant data through the developed Mini App. The PhysRate model code is available at https://***/ShawnTan86/PhysRate. © 2023, The Authors. All rights reserved.

关键词： Video signal processing

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AIM 2019 challenge on real-world image super-resolution: Methods and results

arXiv

引用

arXiv 2019年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Fritsche, Manuel Gu, Shuhang Purohit, Kuldeep Kandula, Praveen Suin, Maitreya Rajagopalan, A.N. Joon, Nam Hyung Won, Yu Seung Kim, Guisik Kwon, Dokyeong Hsu, Chih-Chung Lin, Chia-Hsiang Huang, Yuanfei Sun, Xiaopeng Lu, Wen Li, Jie Gao, Xinbo Bell-Kligler, Sefi Computer Vision Lab ETH Zurich Indian Institute Of Technology Madras India Image Communication & Signal Processing Laboratory Hanyang University Seoul Korea Republic of CVML Chung-Ang University Department Management Information Systems National Pingtung University of Science and Technology Department of Electrical Engineering National Cheng-Kung University Xidian University Weizmann Institute of Science Israel

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the superresolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

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Automatic Multimodal Brain-tumor Segmentation

Automatic Multimodal Brain-tumor Segmentation

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2015 Fifth International Conference on Instrumentation and Measurement,Computer,communication and Control (IMCCC2015)(第五届仪器测量、计算机通信与控制国际会议)

作者： Yisu Lu Wufan Chen Electronic Engineering Department South China Institute of Software Engineering.GU Guangzhou China Key Lab for Medical Image Processing Southern Medical University Guangzhou China

ISBN: (纸本)9781467377249

Brain-tumor segmentation method is an important clinical requirement for the brain-tumor diagnosis and the radiotherapy *** the number of clusters is very difficult to define for high diversity in the appearance of tumor tissue among the different patients and the ambiguous boundaries about the *** our study,the nonparametric mixture of Dirichlet process (MDP) model is used to segment the tumor images automatically,which can be performed without initialization of the clustering ***,the anisotropic diffusion and Markov random field (MRF) smooth constraint are both proposed in our *** segmentation results for the multimodal MR glioma image sequences showed the properties,such as accuracy and computing speed about our algorithm demonstrates very impressive.

关键词： image segmentation Multimodal Brain-tumor Dirichlet process anisotropic diffusion Markov random field

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GMRE-iUnet: Isomorphic Unet fusion model for PET and CT lung tumor images

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 166卷 107514-107514页

作者： Zhou, Tao Zhang, Xiangxiang Lu, Huiling Li, Qi Liu, Long Zhou, Huiyu North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China Ningxia Med Univ Sch Med Informat & Engn Yinchuan 750004 Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH Leics England North Minzu Univ Key Lab Image & Graph Intelligent Processing State Ethn Affairs Commiss Yinchuan 750021 Peoples R China

Lung tumor PET and CT image fusion is a key technology in clinical diagnosis. However, the existing fusion methods are difficult to obtain fused images with high contrast, prominent morphological features, and accurate spatial localization. In this paper, an isomorphic Unet fusion model (GMRE-iUnet) for lung tumor PET and CT images is proposed to address the above problems. The main idea of this network is as following: Firstly, this paper constructs an isomorphic Unet fusion network, which contains two independent multiscale dual encoders Unet, it can capture the features of the lesion region, spatial localization, and enrich the morphological information. Secondly, a Hybrid CNN-Transformer feature extraction module (HCTrans) is constructed to effectively integrate local lesion features and global contextual information. In addition, the residual axial attention feature compensation module (RAAFC) is embedded into the Unet to capture fine-grained information as compensation features, which makes the model focus on local connections in neighboring pixels. Thirdly, a hybrid attentional feature fusion module (HAFF) is designed for multiscale feature information fusion, it aggregates edge information and detail representations using local entropy and Gaussian filtering. Finally, the experiment results on the multimodal lung tumor medical image dataset show that the model in this paper can achieve excellent fusion performance compared with other eight fusion models. In CT mediastinal window images and PET images comparison experiment, AG, EI, QAB/F, SF, SD, and IE indexes are improved by 16.19%, 26%, 3.81%, 1.65%, 3.91% and 8.01%, respectively. GMRE-iUnet can highlight the information and morphological features of the lesion areas and provide practical help for the aided diagnosis of lung tumors.

关键词： Lung tumor Multimodal medical image fusion Isomorphic unet CNN-Transformer Hybrid attention mechanism

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