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arXiv 2017年

作者： Liu, Xiangyu Liu, Qingjie Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China

Remote sensing image fusion (also known as pan-sharpening) aims at generating high resolution multi-spectral (MS) image from inputs of a high spatial resolution single band panchromatic (PAN) image and a low spatial resolution multi-spectral image. Inspired by the astounding achievements of convolutional neural networks (CNNs) in a variety of computer vision tasks, in this paper, we propose a two-stream fusion network (TFNet) to address the problem of pan-sharpening. Unlike previous CNN based methods that consider pan-sharpening as a super resolution problem and perform pan-sharpening in pixel level, the proposed TFNet aims to fuse PAN and MS images in feature level and reconstruct the pan-sharpened image from the fused features. The TFNet mainly consists of three parts. The first part is comprised of two networks extracting features from PAN and MS images, respectively. The subsequent network fuses them together to form compact features that represent both spatial and spectral information of PAN and MS images, simultaneously. Finally, the desired high spatial resolution MS image is recovered from the fused features through an image reconstruction network. Experiments on Quickbird and GaoFen-1 satellite images demonstrate that the proposed TFNet can fuse PAN and MS images, effectively, and produce pan-sharpened images competitive with even superior to state of the arts. Copyright © 2017, The Authors. All rights reserved.

关键词： Image fusion

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An Extended Type Cell Detection and Counting Method based on FCN

An Extended Type Cell Detection and Counting Method based on...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Runkai Zhu Dong Sui Hong Qin Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Computer Science Department Stony Brook University (SUNY) Stony Brook New York

Cell detection and counting are critical and essential tasks for many biological and clinical studies. Traditionally, these tasks are usually performed by visual inspection, which is time consuming and prone to induce subjective bias. These make automatic cell counting and detection essential for large- scale and objective studies. Unfortunately, the hard examples such as cell blur, clutter, bleed-through and imaging noise make these tasks extremely challenging. Over the last few years, automatic cell detection and counting have evolved from earlier methods that are often based on filters to the current state-of- the-art deep learning methods. In this paper, we propose a novel efficient method for robust counting and detection task based on fully convolution networks (FCN). Our method is able to handle most of detection and counting problems from different kinds of cell datasets, and can cover most senior microscopy images, such as bright field, pathology stained material and electron. Extensive experiments on the public and private datasets demonstrate the effectiveness and reliability of our approach.

关键词： Training Microscopy Pathology Biology Convolution Testing

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Wireless Ad Hoc Network Simulation Based on virtual reality technology

Wireless Ad Hoc Network Simulation Based on Virtual Reality ...

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International Conference on virtual reality and Visualization (ICVRV)

作者： Yuanyuan Li Xun Luo Edwin Lobo Andrea Pilco Yi Chen School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer and Information Engineering Beijing Technology and Business University Beijing China

ISBN: (纸本)9781538626375;9781538626368

This paper proposes wireless ad hoc network simulation based on virtual reality technology. Functional components provided by a game engine such as scene management, animation, rendering, scripting, and physics engine can provide suitable interfaces for ad hoc network simulation. Using these components, simulation of ad hoc networks can be easily implemented. The ad-hoc network simulation technology can be carried out in virtual reality by game engine, which is illustrated by advantages of the game engine and the simulation process in this paper. The workability and accuracy of the simulation scheme are demonstrated, with the metrics of comparing the experimental data with other simulation techniques commonly used.

关键词： Solid modeling Games Engines virtual reality Computational modeling Mobile ad hoc networks

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Study on the Effect of Web Color Scheme on User Behavior

Study on the Effect of Web Color Scheme on User Behavior

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International Conference on virtual reality and Visualization (ICVRV)

作者： Shujuan Tian Xun Luo Dan Lu Yi Chen School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer and Information Engineering Beijing Technology and Business University Beijing China

ISBN: (纸本)9781538626375;9781538626368

Internet users spend most of their time reading and using web pages. Because of the reason there is a high demand for the user browsing experience to be pleasant. Experiments show that when people look at objects, the first impression that causes visual response is the color of the object. Therefore, it is of great significance to explore how web color schemes affect user behavior. This paper presents a framework that can automatically recolor the web pages based on reference palettes. Moreover, it analyzes and explores the effect of web color schemes on user behavior through a series of user studies. The preliminary experimental results show that the color scheme has a statistically significant effect on user preferences. The other factors include gender and hearing capability.

关键词： Image color analysis Web pages Visualization virtual reality Auditory system Tools

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Facial age estimation with images in the wild 22nd

Facial age estimation with images in the wild

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Zou, Ming Niu, Jianwei Chen, Jinpeng Liu, Yu Zhao, Xiaoke State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9783319276700

In this paper, we investigate facial age estimation with images in the wild. We aim to utilize images from the Internet to alleviate the problem of imbalance in age distribution. First, we crawl 14,283 images with their context from Wikipedia and infer age labels from the context for each image. After face detection, facial landmark detection and alignment, we build a set of images for facial age estimation, containing 9,456 faces with significant variations. Then, we exploit cost-sensitive learning algorithms including biased penalties SVM and Random forests for age estimation, using images in the wild as the training set. We propose to use the Gaussian function to determine varied misclassification costs. Conducted on two public aging datasets, the within-database experiments illustrate the performance improvement with the introduction of images in the wild. Furthermore, our cross-database experiments validate the generalization capability of proposed cost-sensitive age estimator. © Springer International Publishing Switzerland 2016.

关键词： Face recognition

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Too far to see? Not really! - Pedestrian detection with scale-aware localization policy

arXiv

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arXiv 2017年

作者： Zhang, Xiaowei Cheng, Li Li, Bo Hu, Hai-Miao School of Computer Science and Engineering Beijing Key Laboratory of Digital Media State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Bioinformatics Institute A*STAR Singapore and School of Computing National University of Singapore Singapore

A major bottleneck of pedestrian detection lies on the sharp performance deterioration in the presence of small-size pedestrians that are relatively far from the camera. Motivated by the observation that pedestrians of disparate spatial scales exhibit distinct visual appearances, we propose in this paper an active pedestrian detector that explicitly operates over multiple-layer neuronal representations of the input still image. More specifically, convolutional neural nets such as ResNet [20] and faster R-CNNs [35] are exploited to provide a rich and discriminative hierarchy of feature representations as well as initial pedestrian proposals. Here each pedestrian observation of distinct size could be best characterized in terms of the ResNet feature representation at a certain layer of the hierarchy;Meanwhile, initial pedestrian proposals are attained by faster R-CNNs techniques, i.e. region proposal network and follow-up region of interesting pooling layer employed right after the specific ResNet convolutional layer of interest, to produce joint predictions on the bounding-box proposals' locations and categories (i.e. pedestrian or not). This is engaged as input to our active detector where for each initial pedestrian proposal, a sequence of coordinate transformation actions is carried out to determine its proper x-y 2D location and layer of feature representation, or eventually terminated as being background. Empirically our approach is demonstrated to produce overall lower detection errors on widely-used benchmarks, and it works particularly well with far-scale pedestrians. For example, compared with 60.51% log-average miss rate of the state-of-the-art MS-CNN [47] for far-scale pedestrians (those below 80 pixels in bounding-box height) of the Caltech benchmark, the miss rate of our approach is 41.85%, with a notable reduction of 18.68%. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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Shadow obstacle model for realistic corner-turning behavior in crowd simulation

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Frontiers of Information technology & Electronic Engineering 2016年第3期17卷 200-211页

作者： Gao-qi HE Yi JIN Qi CHEN Zhen LIU Wen-hui YUE Xing-jian LU Department of Computer Science and Engineering East China University of Science and Technology Shanghai 200237 China MOE Key Laboratory of Geographic Information Science East China Normal University Shanghai 200241 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China College of Information Science and Technology Ningbo University Ningbo 315211 China

This paper describes a novel model known as the shadow obstacle model to generate a realistic comer-tuming behavior in crowd simulation. The motivation for this model comes from the observation that people tend to choose a safer route rather than a shorter one when turning a comer. To calculate a safer route, an optimization method is proposed to generate the corner-turning rule that maximizes the viewing range for the agents. By combining psychological and physical forces together, a full crowd simulation framework is established to provide a more realistic crowd simulation. We demonstrate that our model produces a more realistic comer-turning behavior by comparison with real data obtained from the experiments. Finally, we per- form parameter analysis to show the believability of our model through a series of experiments.

关键词： Comer-turning behavior Crowd simulation Safety awareness Rule-based model

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Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

arXiv

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arXiv 2017年

作者： Yu, Lequan Cheng, Jie-Zhi Dou, Qi Yang, Xin Chen, Hao Qin, Jing Heng, Pheng-Ann Dept. of Computer Science and Engineering Chinese University of Hong Kong Department of Electrical Engineering Chang Gung University Taoyuan Taiwan Centre for Smart Health School of Nursing Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coeficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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Towards automatic semantic segmentation in volumetric ultrasound 1

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20th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Yang, Xin Yu, Lequan Li, Shengli Wang, Xu Wang, Na Qin, Jing Ni, Dong Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Department of Ultrasound Affliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical University Shenzhen China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China School of Nursing Centre for Smart Health The Hong Kong Polytechnic University Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9783319661827

ISBN: (纸本)9783319661810

3D ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. However, lacking of efficient tools to decompose the volumetric data greatly limits its widespread. In this paper, we are looking at the problem of volumetric segmentation in ultrasound to promote the volume-based, precise maternal and fetal health monitoring. Our contribution is threefold. First, we propose the first and fully automatic framework for the simultaneous segmentation of multiple objects, including fetus, gestational sac and placenta, in ultrasound volumes, which remains as a rarely-studied but great challenge. Second, based on our customized 3D Fully Convolutional Network, we propose to inject a Recurrent Neural Network (RNN) to flexibly explore 3D semantic knowledge from a novel, sequential perspective, and therefore significantly refine the local segmentation result which is initially corrupted by the ubiquitous boundary uncertainty in ultrasound volumes. Third, considering sequence hierarchy, we introduce a hierarchical deep supervision mechanism to effectively boost the information flow within RNN and further improve the semantic segmentation results. Extensively validated on our in-house large datasets, our approach achieves superior performance and presents to be promising in boosting the interpretation of prenatal ultrasound volumes. Our framework is general and can be easily extended to other volumetric ultrasound segmentation tasks. © 2017, Springer International Publishing AG.

关键词： Ultrasonics

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