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2010 International Conference on E-Product E-Service and E-Entertainment, ICEEE2010

作者： Zhao, Shizhong Feng, Ranran Chen, Donghuo Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai 200062 China Computer Vision Lab. University of Texas at Dallas ECSS 3.621 800 W.Campbell Rd Richardson TX 75080 United States School of Computer Science and Technology Soochow University Suzhou 215006 China

ISBN: (纸本)9781424471614

As the rapid development of computer science technology, programs play an important role in our daily life. given a loop program, whether it can finally terminate is crucial for software safety. However, its terminability is normally considered to be undecidable. In this paper, a particular type of program - univariate monotonic loop program is discussed, and by Backward-Iteration approach, their termination sets can be derived. ©2010 IEEE.

关键词： Trusted computing

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A general approach to model biomedical data from 3D unorganised point clouds with Medial Scaffolds

A general approach to model biomedical data from 3D unorgani...

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2nd Eurographics Workshop on Visualcomputing in Biology and Medicine, EG VCBM 2010

作者： Leymarie, Frederic Fol Chang, Ming-Ching Imielinska, Celina Kimia, Benjamin B. Computing Dept. Goldsmiths College University of London United Kingdom Visualization and Computer Vision Lab. General Electric Global Research Center Niskayuna NY United States Department of Biomedical Informatics Columbia University United States Division of Engineering Brown University United States

ISBN: (纸本)9783905674286

We present the latest developments in modeling 3D biomedical data via the Medial Scaffold (MS), a 3D acyclic oriented graph representation of the Medial Axis (MA) [LK07, SP08]. TheMS (and associated 3DMA) can be computed as the result of the singularities of a geometric wave propagation simulation. We consider here some of the potential applications of this shape model in the realm of biomedical imaging. We can reconstruct complex object surfaces and make explicit the coarse-scale structures, which are ready-to-use in a number of practical applications, including: morphological measurement for cortex or bone thickness, centerline extraction (curve skeleton) for tracheotomy or colonoscopy, surface partitioning for cortical or anatomical surface classification, as well as registration and matching of shapes of tumors or carpal bones. TheMS permits to automatically and efficiently map an unorganised point cloud, i.e., simple 3D coordinates of point samples, to a coherent surface set and associated approximateMA. The derivedMS is used to further recover significant medium and large scale features, such as surface ridges and main axial symmetries. The radius field of theMS provides an intuitive definition for morphological measurements, while the graph structure made explicit by theMS is useful for shape registration and matching applications. © The Eurographics Association 2010.

关键词： Wave propagation

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Interactive Local Color Transfer based on Coupled Map Lattices

Interactive Local Color Transfer based on Coupled Map Lattic...

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11th IEEE International Conference on computer-Aided Design and computer Graphics(第11届IEEE国际计算机辅助设计与图形学学术会议 IEEE CAD/GRAPHICS 2009)

作者： Yi Dong Dan Xu Vision and Media Computing Lab. Department of Computer Science and EngineeringYunnan University Kunming 650091 P. R. China

The paper presents a new interactive local color transformation method base on GrowCut image segmentation algorithm. The method improves Maslennikova et al’s CIM based color transformation algorithm by using Coupled Map Lattices (CML). An intuitive and easy user interface is also supported. User just needs to draft some trails both in reference and target image to make the local color matched. The color transformation method represents some good characteristics auto-repair and segmentation with the usage of GrowCut.

关键词： color transformation coupled cap lattices segmentation GrowCut

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Addressing data bias problems for chest X-ray image report generation

arXiv

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arXiv 2019年

作者： Harzig, Philipp Chen, Yan-Ying Chen, Francine Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg Germany FX Palo Alto Laboratory 3174 Porter Drive Palo AltoCA United States

Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using a dual word LSTM in a hierarchical LSTM model. In addition, we conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. Together with this analysis, we propose a way of selecting a model that generates more distinctive sentences. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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A hypersphere method for plant leaves classification

A hypersphere method for plant leaves classification

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2004 International Symposium on Intelligent multimedia, Video and Speech Processing, ISIMP 2004

作者： Zhang, Guo-Jun Wang, Xiao-Feng Huang, De-Shuang Chi, Zheru Cheung, Yiu-Ming Du, Ji-Xiang Wan, Yuan-Yuan Intelligent Computing Lab. Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230021 China Ctr. for Multimedia Sign. Processing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)0780386884

The recognition of tens of thousands kinds of plants on earth is an important and difficult task. If we use nearest neighbour classifier to solve it, millions of training data are needed to obtain a high correct recognition rate. However, the corresponding classification process would be quite time-consuming. In this paper, we propose a new approach using Moving Median Centers Hypersphere technique to recognize different plant leaves. With this new method, we can successfully decrease the classification time, and reduce the storage size without sacrificing the classification accuracy. The experimental results using real data show that this method is really efficient and effective in classifying the different plant leaves.

关键词： Plants (botany)

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Bark texture feature extraction based on statistical texture analysis

Bark texture feature extraction based on statistical texture...

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2004 International Symposium on Intelligent multimedia, Video and Speech Processing, ISIMP 2004

作者： Wan, Yuan-Yuan Du, Ji-Xiang Huang, De-Shuang Chi, Zheru Cheung, Yiu-Ming Wang, Xiao-Feng Zhang, Guo-Jun Intelligent Computing Lab. Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230021 China Ctr. for Multimedia Sign. Processing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)0780386884

This paper quantitatively describes and discusses the usefulness of texture analysis methods for the recognition of bark Comparative studies of bark texture feature extraction are performed for the four texture analysis methods such as the gray level Run-Length method (RLM), Co-occurrence Matrices method (COMM) and Histogram method (HM) as well as Auto-Correlation method (ACM). Specifically, we use three classifiers of Nearest Neighbor (1-NN), k-Nearest Neighbor (k-NN) and Moving Median Centers (MMC) Hypersphere classifiers to verify the validity of the extracted bark texture features. To gain good result we added the color information that proved very efficient. Moreover, the experimental results also demonstrate that from the viewpoint of the recognition accuracy and computational complexify, the COMM method is superior to the other three methods.

关键词： Feature extraction

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Active Learning for Efficient Sampling of Control Models of Collectives

Active Learning for Efficient Sampling of Control Models of ...

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International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

作者： Alexander Schiendorfer Christoph Lassner Gerrit Anders Wolfgang Reif Rainer Lienhart Institute for Software & Systems Engineering University of Augsburg Germany Perceiving Systems Department Max Planck Institute for Intelligent Systems Germany Multimedia Computing and Computer Vision Lab University of Augsburg Germany

Many large-scale systems benefit from an organizational structure to provide for problem decomposition. A pivotal problem solving setting is given by hierarchical control systems familiar from hierarchical task networks. If these structures can be modified autonomously by, e.g., Coalition formation and reconfiguration, adequate decisions on higher levels require a faithful abstracted model of a collective of agents. An illustrative example is found in calculating schedules for a set of power plants organized in a hierarchy of Autonomous Virtual Power Plants. Functional dependencies over the combinatorial domain, such as the joint costs or rates of change of power production, are approximated by repeatedly sampling input-output pairs and substituting the actual functions by piecewise linear functions. However, if the sampled data points are weakly informative, the resulting abstracted high-level optimization introduces severe errors. Furthermore, obtaining additional point lab.ls amounts to solving computationally hard optimization problems. Building on prior work, we propose to apply techniques from active learning to maximize the information gained by each additional point. Our results show that significantly better allocations in terms of cost-efficiency (up to 33.7 % reduction in costs in our case study) can be found with fewer but carefully selected sampling points using Decision Forests.

关键词： Power generation Production Cost function Schedules Robots Switches

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A kind of SND-based algorithm for self-adapting network congestion control

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Journal of Information and Computational Science 2011年第11期8卷 2033-2044页

作者： Zhang, Degan Hu, Yuxia Liang, Yanpin Liu, Zhaojing Tianjin Key Lab. of Intelligent Computing and Novel software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300191 China

A kind of SND-based algorithm for self-adapting network congestion control has been presented in this paper, which is for the complicated and integrated network environment that the Internet of Things (IOT) to face in the future. As we know, the control of network congestion has become a hot spot in the study of Internet of Things transmission service. The network control algorithm of self-adapting congestion based on semi-normal distribution has improved the original packet loss rate function, and changed congestion situation in Internet of Things. By our many experiments, the validity and correctness of this algorithm has been tested. © 2011 Binary Information Press.

关键词： Internet of things

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Cyber Physical Society with SOA, BPM and Sensor Networks Track Report - CPS 2011

Cyber Physical Society with SOA, BPM and Sensor Networks Tra...

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IEEE International Workshop on Enabling Technologies: Infrastructure for Collab.rative Enterprises (WET ICE)

作者： Zhangbing Zhou Xiaoping Sun Lei Shu Chin-Feng Lai Computer Science Department Institute Telecom & Management SudParis Evry France Institute of Computing Technology Chinese Academy of Sciences Beijing China Nishio Lab. Department of Multimedia Engineering Osaka University Japan Department of Applied Geoinformatics National Ilan University Taiwan

Along with the stronger capability of computing devices and the cheaper and faster communication networks, computing will be embedded in all types of physical resources, and applications with big societal impact and e... 详细信息

关键词： Data models Business Collab.ration Electronic mail Wireless sensor networks Societies Authentication

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Color Object Recognition via Cross-Domain Learning on RGB-D Images

Color Object Recognition via Cross-Domain Learning on RGB-D ...

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IEEE International Conference on Robotics and Automation

作者： Yawen Huang Fan Zhu Ling Shao Alejandro F. Frangi the Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) Department of Electronic and Electrical Engineering The University of Sheffield Sheffield United Kingdom the Multimedia and Visual Computing Lab Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi the Computer Vision and Artificial Intelligence Group Department of Computer Science and Digital Technologies Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781467380270

This paper addresses the object recognition problem using multiple-domain inputs. We present a novel approach that utilizes lab.led RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only relies on RGB images. The highly dissimilar source and target domain data are mapped into a unified feature space through transfer at both feature and classifier levels. In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters simultaneously. The proposed method is trained on a RGB-D Object dataset and evaluated on the Caltech-256 dataset. Experimental results suggest that our approach can lead to significant performance gain over the state-of-the-art methods.

关键词： RGB-D enhancing discriminative capability

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