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International Conference on Space Information Technology

作者： Lu, Li Shi, Baochang Tian, Jinwen Liu, Jian State Education Commission Key Lab for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei 430074 China Inst. for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China Department of Mathematics Huazhong University of Science and Technology Wuhan Hubei 430074 China

Walsh-Haar function system that was first intruoduced by us is a new kind of function systems, and has a good global/local property. This function system is called Walsh ordering function system since its generation kernel functions belong to Walsh ordering Walsh function system. We worked out a recursive property of the matrix WHKRm+1 corresponding to the first KR m+1 Walsh-Haar functions in Walsh-Haar function system, and we also proved that Walsh-Haar function system is perfect and orthogonal similar to Walsh function system and Haar function system. Thus, discrete Walsh-Haar transformation (DW-HT) is an orthogonal transformation that can be widely used in signal processing. In this paper, using the recursive property of the matrix WHKRm+1 and the fast algorithm of discrete Walsh transformation (DWT) in Walsh ordering, we have designed a fast algorithm of Walsh ordering DW-HT based on the bisection technique. The idea and method used in this paper can be used for designing fast algorithms of other ordering DW-HTs and other discrete orthogonal transformations.

关键词： Mathematical transformations

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Portable Health Screening Device of Respiratory Infections

Portable Health Screening Device of Respiratory Infections

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Zheng Jiang Menghan Hu Guangtao Zhai Institute of Image Communication and Information Processing Shanghai Jiao Tong University China Shanghai Key Labora. of Multidim. Infor. Proce East China Normal University China

ISBN: (数字)9781728114859

ISBN: (纸本)9781728114866

The COVID-19 epidemic was listed as a public health emergency of international concern by the WHO on January 30, 2020. To curb the secondary spread of the epidemic, many public places were equipped with thermal imagers to check the body temperature. However, the COVID-19 pneumonia has concealed symptoms: the first symptom may not be fever, and can be shortness of breath. During epidemic prevention, many people tend to wear masks. Therefore, in this demo paper, we proposed a portable non-contact health screening system for people wearing masks, which can simultaneously obtain body temperature and respiration state. This system consists of three modules viz. thermal image collection module, health indicator calculation module and health assessment module. In this system, the thermal video of human faces is first captured through a portable thermal imaging camera. Then, body temperature and respiration state are extracted from the video and imported into the following health assessment module. Finally, the screening result can be obtained. The results of preliminary experiments show that this system can offer an accurate screening result within 15 seconds. This system can be utilized in many application scenarios such as communities and campuses. The demo videos of the proposed system are available at: https://***/10.6084/***.12028032.

关键词： Epidemics Cameras COVID-19 Feature extraction Temperature distribution Biomedical measurement

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Automatic diagnosis of disc herniation based on DenseNet fusion model 8

Automatic diagnosis of disc herniation based on DenseNet fus...

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8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： image segmentation

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An Ikonos image fusion process using support value transform

An Ikonos image fusion process using support value transform

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6th International Conference on Machine Learning and Cybernetics

作者： Zheng, Sheng Shi, Wenzhong Liu, Jian China Three Gorges Univ Inst Intelligent Vis & Image Informat Best Coll Elect Engn & Informat Technol Yichang 443002 Peoples R China Huazhong Univ Sci & Technol Elect & Informat Engn Dept Wuhan 430074 Peoples R China Hong Kong Polytech Univ Adv Res Ctr Spatial Informat Technol Dept Land Surveying & Geo Informat Hong Kong Peoples R China Huazhong Univ Sci & Technol State Educ Commiss Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9781424409723

Panchromatic (Pan)-sharpening of multispectral (MS) bands is an important technique in various applications of satellite remote sensing. In this paper, We apply the support value transform (SVT) to Ikonos image fusion. The fused saliency features are represented by support values and extracted by SVT. The low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan image. The fusing results on Ikonos MS + Pan data demonstrate that the proposed image fusion method is effective and efficient.

关键词： mapped least squares support vector machine (mapped LS-SVM) support value transform (SVT) Ikonos image fusion

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Hierarchical reinforcement learning for saliency detection of low-resolution airports

Hierarchical reinforcement learning for saliency detection o...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： au Zhao au Ma au Wang au Jiang Beijing Key Laboratory of Digital Media Haidian District Beijing China Image Processing Center Beihang University Beijing China Beijing Jinghang Research Institute of Computing and Communication Beijing China

The traditional airport detection methods usually utilize geometric characteristics, which are limited by large amount of data and low-resolution of the remote sensing images. In this paper, we present a novel hierarchical reinforcement learning (HRL) saliency model for quickly airports detecting in large cover area. In contrast with conventional saliency models which usually are effective for high-resolution nature images, our method learns hierarchically high-level features via multi-scale superpixels segmentation and Least Absolute Shrinkage and Selection Operator (LASSO). More importantly, we introduce back-propagation theory for hierarchical learning to adaptively control and generate saliency map. Therefore our unsupervised saliency model is more simple and effective for low-resolution airport detection. Compared with 18 state-of-the-art saliency models, experimental results demonstrate the excellent performance of our method on the remote sensing image datasets. It is more robust and accurate for long-range airports detection.

关键词： Airports Atmospheric modeling Remote sensing Adaptation models image reconstruction Computational modeling Feature extraction

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Traffic speed measurement using Spatio-Temporal model and frequency domain analysis

Traffic speed measurement using Spatio-Temporal model and fr...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Arucha Rungchokanun Vutipong Areekul Supakorn Siddhichai Hiroaki Kunieda Kasetsart Signal & Image Processing Laboratory (KSIP Lab) Department of Electrical Engineering Kasetsart University Thailand Image Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Communication and Integrated Systems Tokyo Institute of Technology Meguro Tokyo Japan

In traffic monitoring applications, traffic speed is an important parameter of traffic management. The method for traffic speed measurement using video based on Spatio-Temporal (ST) model and frequency domain analysis is proposed in this paper. This method is designed to be able to measure the traffic speed in every pattern of road. The novel of proposed method is unfolding the edge information on ST model and analyse them in frequency domain to determine traffic speed. The proposed method is evaluated by compare with actual speed. The traffic speed is measured accurately 97.1%.

关键词： Analytical models Sensors Pixel image sensors Monitoring

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A Novel LOS Rate Estimation Method Based on images for Strap-down Inertial Guidance 3

A Novel LOS Rate Estimation Method Based on Images for Strap...

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2020 3rd International Conference on Advanced Algorithms and Control Engineering, ICAACE 2020

作者： Xu, Zheng Luo, Haibo Hui, Bin Chang, Zheng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science Shenyang110016 China Key Lab of Image Understanding and Computer Vision Shenyang110016 China

With the development of technology, precision guided weapon is becoming more and more important in modern war. In order to launch our recent guidance system on medium and small guided weapons, we propose a method to obtain the LOS rate by combining information from both camera and gyroscope. To be specific, we firstly calculate the body LOS angle through transforming the image pixel coordinate system into the image physical coordinate system according to camera internal parameters;then subtract the missile motion information contained in the measurement signal of the seeker and finally the LOS rate is deduced. Comparing with traditional gimballed seekers, our strap-down seekers with camera have significantly reduced costs and influences caused by external environments on platform. © Published under licence by IOP Publishing Ltd.

关键词： Cameras

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis

arXiv

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arXiv 2022年

作者： Li, Xingyi Hong, Chaoyi Wang, Yiran Cao, Zhiguo Xian, Ke Lin, Guosheng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China S-Lab Nanyang Technological University Singapore

We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fail to recover the fine appearance details, especially in self-occluded areas. This is because a single view only provides limited information. We observe that man-made objects usually exhibit symmetric appearances, which introduce additional prior knowledge. Motivated by this, we investigate the potential performance gains of explicitly embedding symmetry into the scene representation. In this paper, we propose SymmNeRF, a neural radiance field (NeRF) based framework that combines local and global conditioning under the introduction of symmetry priors. In particular, SymmNeRF takes the pixel-aligned image features and the corresponding symmetric features as extra inputs to the NeRF, whose parameters are generated by a hypernetwork. As the parameters are conditioned on the image-encoded latent codes, SymmNeRF is thus scene-independent and can generalize to new scenes. Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transformation, and demonstrates good generalization when applied to unseen objects. Code is available at: https://***/xingyi-li/SymmNeRF. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Analyzing Occupational Stress Based On Inner Composition Alignment Algorithm

Analyzing Occupational Stress Based On Inner Composition Ali...

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International Congress on image and Signal processing, BioMedical Engineering and Informatics

作者： Zhen Shi Shitong Wang Jun Wang Jiafei Dai Fengzhen Hou Jin Li Image Processing and Image Communications Key Lab Nanjing Univ. of Posts & Telecomm China Nanjing Brain Hospital China School of Science China Pharmaceutical University Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China

ISBN: (纸本)9781509037117

To distinguish different level of occupational stress of students and nurses, this paper applied inner composition alignment (IOTA) algorithm to analyse differences between the students' Electroencephalogram (EEG) and the nurses' EEG and to explore the influence of occupational stress on EEG. First, we selected nine cases of students EEG data and the same quantity of nurses EEG data from General Hospital of Nanjing Military Region, then, constructed two different brain networks by utilizing IOTA method and analyzed the networks characteristics which include IOTA coefficient, Clustering coefficient and Average degree. In order to verify the accuracy of the results, we used SPSS to analyse the significant difference hypothesis testing. The IOTA coefficients are inconsistent between students' EEG and nurses' EEG. Compared with the students, nurses' brain network is more complex. The level of occupational stress of nurses is higher than students. The IOTA algorithm can significantly distinguish occupational stress between students and nurses, and all results demonstrated the validity of the algorithm.

关键词： Brain Network Occupational Stress EEG IOTA Complex Network

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