检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Liu, Wei Zhang, Pingping Huang, Xiaolin Yang, Jie Shen, Chunhua Reid, Ian School of Computer Science University of Adelaide Adelaide5005 Australia School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Institute of Image Processing and Pattern Recognition & Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Institute of Image Processing and Pattern Recognition & Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing speed: the high smoothing quality usually requires a high computational cost which leads to the low processing speed. In this paper, we propose a new global optimization based method, named iterative least squares (ILS), for efficient edge-preserving image smoothing. Our approach can produce high-quality results but at a much lower computational cost. Comprehensive experiments demonstrate that the propose method can produce results with little visible artifacts. Moreover, the computation of ILS can be highly parallel, which can be easily accelerated through either multi-thread computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is able to process images of 1080p resolution (1920 × 1080) at the rate of 20fps for color images and 47fps for gray images. In addition, the ILS is flexible and can be modified to handle more applications that require different smoothing properties. Experimental results of several applications show the effectiveness and efficiency of the proposed method. The code is available at https://***/wliusjtu/Real-time-image-Smoothing-via-Iterative-Least-Squares Copyright © 2020, The Authors. All rights reserved.

关键词： Textures

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CTCNet: A CNN-Transformer Cooperation Network for Face image Super-Resolution

arXiv

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arXiv 2022年

作者： Gao, Guangwei Xu, Zixiang Li, Juncheng Yang, Jian Zeng, Tieyong Qi, Guo-Jun The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China The Center for Mathematical Artificial Intelligence Department of Mathematics The Chinese University of Hong Kong Hong Kong Research Center for Industries of the Future School of Engineering Westlake University OPPO Research Seattle United States

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://***/IVIPLab/CTCNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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Polarization based invisible barcode display 14th

Polarization based invisible barcode display

引用

14th International Forum of Digital TV and Wireless Multimedia communication, IFTC 2017

作者： Chen, Qi Chen, Yuanchun Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789811081071

QR Codes (Quick Response) have become a fast and effective way to convey information. Users can get access to a website, a picture, a phone number or any other information they are interested in simply by scanning the QR Codes. However, QR Codes are meaningful only to the mobile devices and naked-eye users can only see a permutation of black dots. The traditional QR Codes mode leads to spatial waste on the screen and makes the screen not appealing. This paper presents a new QR Codes embedding in system based on polarization and Spacial-temporal psychovisual modulation (STPVM). Using the fusion feature of human visual system (HVS), we separate the users’ view from the smartphone camera view and make the QR Codes totally transparent to human. At the same time, it can also be detected by the smartphone camera. Based on this system, we can realize many applications such as real-time advertisement of the merchandise shown on sceen inserted into a film etc. This paper shows a prototype system of invisible QR Codes aiming to proof and validate the theory of the system and this system can be improved in future works. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Polarization

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Indoor Localization System for Individuals with Visual Impairment 14th

Indoor Localization System for Individuals with Visual Impai...

引用

14th International Forum of Digital TV and Wireless Multimedia communication, IFTC 2017

作者： Zeng, Yi Li, Duo Zhai, Guangtao Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789811081071

There are millions of people with visual diseases around the world, most of whom are suffering from inconvenience in their daily lives. In this paper, we demonstrated an indoor localization system for people with visual impairments to help them finding out the right directions without others’ assistance. The system contains three main parts, the hidden display module, the localization module and direction-giving component. Based on temporal psychovisual modulation, hidden display module enables the system to obtain the information required without disturbing normal people. Localization is based on a pair of wearable smart glasses that captures and analyses the surrounding environment. The direction-giving function works by feeding direction back to the user with haptic signals. We test our system by simulating indoor environment and collecting location data. We find out in the same situation, using our system can make subjects find the right places in shorter time compared with random guess. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Angel Girl of Visually Impaired Artists: Painting Navigation System for Blind or Visually Impaired Painters

Angel Girl of Visually Impaired Artists: Painting Navigation...

引用

IEEE Visual communications and image processing (VCIP)

作者： Hang Liu Menghan Hu Guangtao Zhai Huijing Huang Wa Zhang Qingli Li Yinghong Tian Yanling Shi Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China Institute of Image Communication and Information Processing Shanghai Jiao Tong University China

For those who love painting but unfortunately have visual impairments, holding a paintbrush to create a work is really a difficult task. For the purpose of solving this problem, a painting navigation system for visually impaired painters is introduced through the live demonstration. When painting, the developed system can endow visually impaired persons with the ability to perceive the surrounding environment, thus helping them realize their dream of painting. To achieve this goal, we designed four main modules viz., QR code based drawing board positioning module, brush real-time positioning module, color recognition module and human-computer interaction module, and integrated them into the system. In the validation experiments, the blindfolded users can successfully create a painting with the help of the developed navigation system. Moreover, the users told us that this system provided them with good experience. In a way, this painting navigation system can be seen as "angel's eyes" of visually impaired painters. The demo video of the proposed painting navigation system is available at: https://***/10.6084/***.9760004.v1.

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Probabilistic image Quality Assessment: An Economics Point of View

Probabilistic Image Quality Assessment: An Economics Point o...

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IEEE International Symposium on Intelligent Signal processing and communication Systems (ISPACS)

作者： Guangtao Zhai Xiaoping Zhang Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Ryerson University Toronto Canada

We analyze the problem of image quality assessment from economics point of view with two main arguments: 1) Visual contents is a kind of product (good/service) to be utilized by consumers and therefore the image quality assessment process can be modeled using the tools from economics, e.g the law of diminishing marginal utility/returns, the expected utility theory and the principle of risk aversion. 2) The subjective quality rating of a given visual signal is better depicted with a probability distribution than a deterministic value.

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A Real-time Algorithm for Regional Crowd Counting Based on Survival Analysis Theory 4

A Real-time Algorithm for Regional Crowd Counting Based on S...

引用

2019 4th Annual International Conference on information System and Artificial Intelligence, ISAI 2019

作者： Wang, Jun Fu, Yongfeng Ma, Lianhang Yang, Xiaokang Dong, Jiangping Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China 52nd Institute of China Electronics Technology Group Corporation Hangzhou China Zhijiang College Zhejiang University of Technology Hangzhou China School of Economics Zhejiang Gongshang Technology Hangzhou China

This paper proposes a real-time algorithm for regional crowd counting based on survival analysis theory. Our algorithm can accurately calculate regional people flow in real time over a long period, which considers tourists' stopping time as a type of survival time, and discretizes its continuous distribution probability as a proportion of stopping people in a monitoring region. Compared to other crowd counting methods, our algorithm calculates regional population in real time using a mathematical statistical model, which greatly reduces the dependence on hardware devices. Experiments in two real scenarios verify that the average accuracy of our algorithm is over 91% in comparison with 60% of sensing devices. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Population statistics

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Cross-receptive Focused Inference Network for Lightweight image Super-Resolution

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Combining visible light and infrared imaging for efficient detection of respiratory infections such as covid-19 on portable device

arXiv

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arXiv 2020年

作者： Jiang, Zheng Hu, Menghan Fan, Lei Pan, Yaling Tang, Wei Zhai, Guangtao Lu, Yong Institute of Image Communication and Information Processing Shanghai Jiao Tong University China Shanghai Key Labora. of Multidim. Infor. Proce East China Normal University China Rui Jin Hospital/Lu Wan Branch School of Medicine Shanghai Jiao Tong University

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status on respiratory with the accuracy of 83.7% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic. The demo

关键词： Coronavirus

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Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

SSRN

引用

SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

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