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A study on key technologies of unmanned driving

CAAI Transactions on Intelligence Technology 2016年第1期1卷 4-13页

作者： Xinyu Zhang Hongbo Gao Mu Guo Guopeng Li Yuchao Liu Deyi Li Department of Computer Science and Technology Tsinghua University Beijing 100084 China Information Technology Center Tsinghua University Beijing 100084 China State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China The Institute of Electronic System Engineering Beijing 100039 China

Although the development of machine intelligence is far from simulating all the cognitive competence of our brains, still it is absolutely possible to peel the driving activity from people＇s cognitive activities and then make the machine finish some low-level, complicated and lasting driving cognition by simulating our brains. The goal of driving is to replace drivers and free them from boring driving activities. Based on some studies on unmanned driving, this paper summarizes and analyzes the background, significance, research status and key technology of unmanned driving and the research group also introduces some research on brain cognition of driving and sensor placement of intelligent vehicles, which offers more meaningful reference to push the study of unmanned driving.

关键词： Unmanned driving Brain cognition of driving Sensor placement Formalization of brain cognition

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Novel Detection Service Method for Moving Object in IOT

Novel Detection Service Method for Moving Object in IOT

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2016 3rd International Conference on Materials engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Jie Chen Degan Zhang School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education

In embedded Internet of Things(IOT) environment, there are the troubles such as complex background, illumination changes, shadows and other factors for detecting moving object, so we put forward a new detection service method through mixing Gaussian Mixture Model(GMM), edge detection service method and continuous frame difference method in this paper. In time domain, the new method uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection service method which mixes edge detection service method, continuous frame difference method and GMM to get initial contour of moving object, and gets ultimate moving object. This method not only can well adapt to the illumination gradients and background disturbance occurred on scene, but also can well solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional method. As experimental result showing, this method holds better real-time and robustness. It is not only easily implemented, but also can accurately detect moving object.

关键词： GMM embedded IOT moving object detection service

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Urban traffic congestion prediction using floating car trajectory data 15th

Urban traffic congestion prediction using floating car traje...

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Yang, Qiuyuan Wang, Jinzhong Song, Ximeng Kong, Xiangjie Xu, Zhenzhen Zhang, Benshi School of Software Dalian University of Technology Dalian116620 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Key Laboratory of Control Engineering of Henan Province Henan Polytechnic University Jiaozuo454000 China

ISBN: (纸本)9783319271217

Traffic congestion prediction is an important precondition to promote urban sustainable development. Nevertheless, there is a lack of a unified prediction method to address the performance metrics, such as accuracy, instantaneity and stability, systematically. In the paper, we propose a novel approach to predict the urban traffic congestion efficiently with floating car trajectory data. Specially, an innovative traffic flow prediction method utilizing particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens’ cognitive congestion states. We conduct extensive experiments with real floating car data and the experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability. © Springer International Publishing Switzerland 2015.

关键词： Traffic congestion

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Joint modeling of user check-in behaviors for point-of-interest recommendation 15

Joint modeling of user check-in behaviors for point-of-inter...

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24th ACM International Conference on information and Knowledge Management, CIKM 2015

作者： Yin, Hongzhi Zhou, Xiaofang Shao, Yingxia Wang, Hao Sadiq, Shazia University of Queensland School of Information Technology and Electrical Engineering QLD4072 Australia School of Computer Science and Technology Soochow University China School of EECS Peking University China Science and Technology on Integrated Information System Laboratory State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781450337946

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting locations, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, a growing line of research has exploited the temporal effect, geographical-social influence, content effect and word-of-mouth effect. However, current research lacks an integrated analysis of the joint effect of the above factors to deal with the issue of data-sparsity, especially in the out-of-town recommendation scenario which has been ignored by most existing work. In light of the above, we propose a joint probabilistic generative model to mimic user check-in behaviors in a process of decision making, which strategically integrates the above factors to effectively overcome the data sparsity, especially for out-of-town users. To demonstrate the applicability and flexibility of our model, we investigate how it supports two recommendation scenarios in a unified way, i.e., home-town recommendation and out-of-town recommendation. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets in terms of both recommendation effectiveness and efficiency, and the experimental results show its superiority over other competitors. © 2015 ACM.

关键词： Location based services

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A novel naive bayes classifier model based on differential evolution 11th

A novel naive bayes classifier model based on differential e...

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11th International Conference on Intelligent Computing, ICIC 2015

作者： Li, Jun Fang, Guokang Li, Bo Wang, Chong College of Computer Science and Technology Wuhan University of Science and Technology WUST Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System WUST Wuhan430065 China State Key Laboratory of Software Engineering Wuhan University Wuhan430072 China

ISBN: (纸本)9783319221793

Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent assumption of its attribute limits the application of the actual data. This paper presents an approach called Differential Evolution-Naive Bayes (DE-NB) which takes advantage of combining differential evolution with naive Bayes for attribute selection to improve naive Bayes classifier. This method applies DE firstly to search out an optimal subset of attributes reduction in the original attribute space, and then constructs a naive Bayes classifier on the gotten subset of the attributes reduction. Nineteen experimental results on UCI datasets distinctly show that compared with Cfs-BestFirst algorithm, NB algorithm, Support Vector Machine (SVM) algorithm, Decision Tree (C4.5) algorithm, K-neighbor (KNN) algorithm, the proposed algorithm has higher classification accuracy. © Springer International Publishing Switzerland 2015.

关键词： Feature Selection

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Calibration of a three-dimensional photodynamic therapy illumination system and its segmentation assessment for port-wine stains

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Photonics and Lasers in Medicine 2016年第3期5卷 195-202页

作者： Feng, Yun Hu, Xiaoming Zhou, Ya Wang, Yong School of Software Beijing Institute of Technology No. 5 Zhong Guan Cun South Street Haidian District Beijing China School of Life Science Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology No. 5 Zhong Guan Cun South Street Haidian District Beijing China School of Optoelectronics Beijing Institute of Technology No. 5 Zhong Guan Cun South Street Haidian District Beijing China

The uniformity of light dosimetry is an important parameter that affects the efficacy of photodynamic therapy (PDT). Although this uniformity can be improved by a three-dimensional (3D) digital PDT illumination system, it has a low field-of-view (FOV) utilization rate. A checkerboard calibration method using color coding is proposed to calibrate both the projector and camera of the system with a broad common FOV. Experiments reveal that the proposed method increases the utilization rate by up to three times compared with noncolor-coding methods with almost the same accuracy. A fine distinction of phantom lesions in the 3D system can be obtained by clustering, which may be used to optimize the treatment and light-dosimetry evaluation. © 2016 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Calibration

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Portrait image segmentation based on improved grabcut algorithm 6th

Portrait image segmentation based on improved grabcut algori...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Li, Shuai Zheng, Xiaohui Chen, Xianjun Zhan, Yongsong Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Guilin Guangxi541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin Guangxi541004 China Information Engineering School Haikou College of Economics Haikou Hainan571127

ISBN: (纸本)9783319204680

Traditional computer portrait caricature system mainly take the method that exaggerate and deform real images directly, that lead the facial image background also been deformed when exaggerate facial image. If in pretreatment stage, we segmented the characters and background of the input image, and then do the subsequent processing, the problem may be solved. But for better portrait caricature effects, we need an excellent segmentation algorithm. So, we propose an improved Grabcut image segmentation algorithm and use it to extract the prospect character image for exaggeration and deformation. In practical application, we separate deform and exaggerate the foreground characters image with TPS method, then fuse it with the original or new background picture, get the final image. Application proves, the method solves the background deformation problem well, and improves the quality and rate of image segmentation, caricature synthesis effect reality and natural. © Springer International Publishing Switzerland 2015.

关键词： Image segmentation

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Face recognition based on feature selection strategy

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Journal of information and Computational Science 2014年第12期11卷 4203-4210页

作者： Tang, Hengliang Sun, Yanfeng Zhu, Jie Zhao, Mingru Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Transportation Engineering Beijing University of Technology Beijing China Beijing Key Laboratory of Intelligent Logistics System School of Information Beijing Wuzi University Beijing China

In order to extract the effective and discriminant face features, a useful feature selection strategy is designed for face representation. The strategy can remove the facial redundancies, extract the discriminant features, promote the calculation efficiency, and also guarantee the feasibility of the recognition framework. Based on this, the sparse representation framework is improved to collect all the face features and match the recognition task. The experiments, tested on ORL, YALE, CMU-PIE and FERET face databases, demonstrate that the proposed method is effective and robust to facial pose, expression and illumination conditions to some extent. © 2014 Binary information Press

关键词： Face recognition

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Extraction of bridges from high resolution remote sensing image based on topology modeling 2

Extraction of bridges from high resolution remote sensing im...

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2nd IEEE International Conference on Cloud engineering, IC2E 2014

作者： Lu, Haitao Deng, Yong Huang, Zhijian Zhang, Jinfang Science and Technology on Integrated Information System Laboratory Institute of Software CAS China School of Electronic Science and Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9781479937660

Bridges are the hubs of transportation, so it is important to identify and locate bridges for satellite image interpretation. This paper proposes a new method of bridge extraction from high resolution remote sensing images. Firstly, river regions are segmented and road lineaments are extracted. Then, bridge region is represented as intersection of river with roads or roads with roads by using the recognition model proposed in this paper. Finally, a rule-based procedure is applied to verify candidate regions. The experiment results show that, not only bridges over river but also the overpasses can be extracted effectively. The method mainly uses structural information of lineaments on roads and the topological relations in bridge regions, and does not rely on accurate results of river segmentation, so it is robust for complex scenes. © 2014 IEEE.

关键词： Rivers

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