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Visual Detection and Tracking Based on Fusion Saliency

Journal of Physics: Conference Series 2018年第1期1069卷 012161-012161页

作者： Guo, Xinxing Guo, Baolong Cheng, Li Han, Jie Institute of Intelligent Control and Image Engineering Xidian University School of Aerospace science and technology No. 2 South Taibai Road Xi’an Shaanxi China.

In order to improve the ability of target tracking effectively, an improved MeanShift algorithm is proposed in this paper. The algorithm combines the saliency map from the computer and from the human vision through Kinect, so that it can obtain the fusion saliency. Then an image segmentation algorithm is adopted to get the target, which is modelled for an improved MeanShift tracking framework. Experimental results show that the algorithm can significantly improve the accuracy.

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Infrared image Super-Resolution Reconstruction via Sparse Representation

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Zuming Chen Baolong Guo Qi Zhang Cheng Li Institute of Intelligent Control and Image Engineering Xidian University School of Aerospace science and technology No. 2 South Taibai Road Xi'an Shaanxi China.

Infrared imaging equipment can work at night and in complex environments, but the resolution of infrared image is not good. Super-resolution (SR) is a very important technology in image processing. It can reconstruct the low-resolution image by signal processing without changing the hardware device. This paper presents a novel infrared super-resolution reconstruction algorithm, which is based on sparse representation. Experiment results show that, compared with interpolation based approach, the proposed algorithm shows a better performance in infrared scenarios.

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Synchronization of Switched Neural Networks with Communication Delays via the Event-Triggered control

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IEEE Transactions on Neural Networks and Learning Systems 2017年第10期28卷 2334-2343页

作者： Wen, Shiping Zeng, Zhigang Chen, Michael Z. Q. Huang, Tingwen Guangdong Province Key Lab of Digital Manufacturing Equipment Key Laboratory of Image Processing Intelligent Control of Education Ministry of China School of Automation Guangdong HUST Industrial Technology Research Institute Huazhong University of Science Technology Wuhan Hubei430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong Texas A M University at Qatar DohaTX5825 United States

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results. © 2012 IEEE.

关键词： Synchronization

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Object Classification Based on Radon Transform

Object Classification Based on Radon Transform

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作者： Jing Ma Baolong Guo Institute of Intelligent Control and Image Engineering Xidian University

With the development of science and technology, the speed of taking and transmitting images has greatly improved, so that the target recognition technology based on remote sensing image becomes a reality. According to the deficiencies of the previous recognition methods, a method of automatic threshold is proposed for getting more accurate features about the positioned object, then the feature extraction by SVM classifier for classification and recognition. Experiments demonstrate the effectiveness of the current recognition system. The system provides a good performance in aircraft recognition and classification, at the same time it also offers better robustness.

关键词： Automatic Target Recognition(ATR) Radon Transform Support Vector Machine(SVM) Classifier

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An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse

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Journal of intelligent Manufacturing 2016年第2期27卷 389-408页

作者： Chen, Fangyu Wang, Hongwei Xie, Yong Qi, Chao Key Laboratory of Image Processing and Intelligent Control of Education Ministry Institute of Systems Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China

One of the challenging problems in order picking is how to deal with the congestion happens in warehouse with multiple pickers. In this paper, we consider an ant colony optimization (ACO)-based online routing method to find picking routes for multiple order pickers under nondeterministic picking time. Here, a default route is formed by ACO for each single picker. Then, we coordinate these routes to alleviate congestion by dedicated rules based on indoor positioning and information sharing technologies, during order pickers serve the picking task. Our results indicate that the proposed method can achieve a reduction in the order service time primarily by coping with the congestion. We conclude that the new method is particularly effective in multiple-block picker-to-parts warehouses. © 2014, Springer Science+Business Media New York.

关键词： Ant colony optimization Congestion Multiple order pickers Routing method Warehouse management

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Derivation languages of splicing P systems 12th

Derivation languages of splicing P systems

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12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Mahalingam, Kalpana Paul, Prithwineel Song, Bosheng Pan, Linqiang Subramanian, K.G. Department of Mathematics Indian Institute of Technology Madras Chennai600036 India Key Laboratory of Image Information Processing Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China School of Electric and Information Engineering Zhengzhou University of Light Industry ZhengzhouHenan450002 China Faculty of Science Liverpool Hope University LiverpoolL16 9JD United Kingdom

ISBN: (纸本)9789811071782

Labelled splicing P systems are distributed parallel computing models, where sets of strings that evolve by splicing rules are labelled. In this work, we consider labelled splicing systems with the following modifications: (i) The strings in the membranes are present in arbitrary number of copies;(ii) the rules in the regions are finite in number. Results on the language family generated by the labelled splicing system in comparison with the language families of the Chomsky hierarchy, including recursively enumerable languages, are obtained, by involving only either one or two membranes in the P systems considered. © Springer Nature Singapore Pte Ltd 2017.

关键词： Distributed computer systems

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LSP image reconstruction algorithm in electrical capacitance tomography

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ICIC Express Letters, Part B: Applications 2016年第8期7卷 1673-1678页

作者： Zhao, Yulei Guo, Baolong Institute of Intelligent Control and Image Engineering Xidian University No. 2 Taibai South Road Xi’an710071 China

To solve the problem of the “soft-field” effect and the proximity of sensitivity matrix in electrical capacitance tomography technology, an image reconstruction algorithm based on least squares support vector machine (LS-SVM) and particle swarm optimization (PSO) has been developed, namely LSP algorithm. Group image samples of some typical flow patterns are trained, and the capacitance deviation generated by the proximity of the sensitivity matrix is predicted. On the basis of the deviation, the fitness function in particle swarm optimization is established. The nonlinear adjustment method for inertia factor and the mutation operation of particles speed are used in the standard particle swarm optimization. The improved PSO algorithm is used to search for the optimal solution of the reconstructed image. The simulation shows that compared with Landweber algorithm, the image precision of LSP algorithm is improved nearly 50%, and the image error decreases to less than 20% for some typical flow patterns. Therefore, LSP algorithm can be applied to the non-real-time ECT system with high precision. This paper presents a feasible and effective way to research on image reconstruction algorithm for ECT. © 2016 ICIC International.

关键词： Flow patterns

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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