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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Automated region growing for segmentation of brain lesion in diffusion-weighted MRI

Automated region growing for segmentation of brain lesion in...

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2012 International MultiConference of Engineers and computer Scientists, IMECS 2012

作者： Mohd Saad, N. Abu-Bakar, S.A.R. Muda, Sobri Mokji, M. Abdullah, A.R. Electronics and Computer Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia Computer Vision Video and Image Processing Research Lab. Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Radiology Department Medical Faculty Universiti Kebangsaan Malaysia Jalan Yaacob Latif 56000 Cheras Kuala Lumpur Malaysia Electrical Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

ISBN: (纸本)9789881925114

This paper presents an automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) using region growing approach. The lesions are acute infarction, haemorrhage, tumour and abscess. Region splitting and merging is used to detect the lesion region. Then, histogram thresholding technique is applied to automate the seeds selection. The region is iteratively grown by comparing all unallocated neighbour pixels to the seeds. The difference between pixel's intensity value and the region's mean is used as the similarity measure. Evaluation is made for performance comparison between automatic and manual seeds selection. Overall, automated region growing algorithm provides comparable results with the semi-automatic segmentation.

关键词： Magnetic resonance imaging

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Speed-Sensitive Weighting Algorithm for Digital Beamforming of Adaptive Antenna Arrays

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Wireless engineering and Technology 2011年第3期2卷 165-174页

作者： Shahriar Shirvani Moghaddam Mahyar Shirvani Moghaddam Digital Communications Signal Processing (DCSP) Research Lab. Faculty of Electrical and Computer Engineering Shahid Rajaee Teacher Training University (SRTTU) Telecommunication Lab. School of Electrical and Information University of Sydney

One of the main objectives of adaptive antenna array processing is reducing the computational complexity and convergence time in a joint state. This article proposes a speed-sensitive adaptive algorithm for estimating the weights of smart antenna systems based on least mean squares (LMS) or constant modulus (CM) algorithms. According to the next estimated location as well as the source velocity, this novel proposed weighting algorithm selects those weights that have a higher effect on the radiation pattern and will then form the antenna pattern by only changing these weights. In this research, 3 versions of the new algorithm named as: Not-zero (Leaves half number of weights as it is the other half), Zero (Sets half number of weights to be zero and estimates other half), and Updating (Leaves half of weights unchanged and estimates other half in one phase and updates all weights in the next phase) are proposed. Through simulation of these 3 versions of speed-sensitive algorithms and comparing among conventional full weight LMS and CM algorithms, new LMS-based and CM-based algorithms have been finally proposed that offer reduced complexity and acceptable performance at different signal to noise ratios (SNRs). In this investigation, three channel scenarios are simulated which are as follows: pure noisy channel, channel with one interferer and channel with two interferers. In accordance with the simulation results, an appropriate algorithm based on weighting half number of array elements and updating all existing weights between two consecutive times to avoid error propagation effect has been proposed.

关键词： Beamforming Adaptive Antenna Array LMS CM Training-Based Blind

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Brain tissue segmentation using an unsupervised clustering technique based on PSO algorithm

Brain tissue segmentation using an unsupervised clustering t...

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17th Iranian Conference in Biomedical engineering, ICBME 2010

作者： Azarbad, Milad Ebrahimzadeh, Ataollah Babajani-Feremi, Abbas Faculty of Electrical and Computer Engineering BABOL University of Technology Babol Iran Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit United States

ISBN: (纸本)9781424474844

image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional genetic algorithms. The proposed technique is based on the participle swarm optimization (PSO) and, in fact, is an unsupervised clustering method based on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not need to be known in advance. We evaluate and validate performance of the proposed method using simulation studies. The simulation results show that the accuracy of the proposed method is about 96%. © 2010 IEEE.

关键词： Magnetic resonance

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Speckle and CCD noise in rotationally symmetric and anamorphic laser triangulation

Speckle and CCD noise in rotationally symmetric and anamorph...

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4th International Symposium on Precision Mechanical Measurements

作者： Wang, Lei Gao, Jun Eckstein, Johannes Ott, Peter Dept. of Mechanical and Electrical Engineering Xiamen University Xiamen 361005 China Lab. of Image Information Processing School of Computer and Information Hefei University of Technology Hefei 230009 China Optical Engineering Lab. Heilbronn University Heilbronn 74081 Germany

As one kind of anamorphic laser triangulation displacement sensor, rotationally symmetric triangulation (RST) has a lot of advantages comparing to traditional ones. Laser speckle and CCD noise are also two fundamental uncertainty factors in this kind of sensors. The analytic expression of centroid uncertainty limit from speckle in RST sensor is derived in this paper. It is shown that the uncertainty limit in RST, and also in anamorphic triangulation, is dependent on the solid angle subtended by entrance pupil as seen from the illuminated laser spot, as well as the laser wavelength. Because it's very easy to get a bigger entrance pupil in RST than in traditional laser triangulation, the centroid uncertainty limit from speckle in RST is much smaller. The CCD noise in RST and anamorphic laser triangulation is dependent on photon shot noise, dark current, photo-response non-uniformity, and cross talk. The centroid uncertainty limit from CCD noise in this case is described by 1-D Cramer-Rao lower bound, and is also smaller than in traditional triangulation. By using more CCD pixels, RST is less sensitive to noise level. Results of simulation and experiments verify the result of deriving. © 2008 SPIE.

关键词： Speckle

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Eccentric error and compensation in rotationally symmetric laser triangulation

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仪器仪表学报 2007年第9期28卷 1548-1553页

作者： Wang Lei Gao Jun Wang Xiaojia Johannes Eckstein Peter Ott Lab of Image Information Processing School of Computer and Information Hefei University of Technology Hefei 230009 China Optical Engineering Lab. Heilbronn University Heilbronn D-74081 Germany Dep. of Mechanical and Electrical Engineering Xiamen University Xiamen 361005 China

Rotationally symmetric triangulation(RST)sensor has more flexibility and less uncertainty limits because of the abaxial rotationally symmetric optical *** if the incident laser is eccentric,the symmetry of the image will descend,and it will result in the eccentric error especially when some part of the imaged ring is *** model of rotationally symmetric triangulation that meets the Schimpflug condition is presented in this *** error from eccentric incident laser is *** is pointed out that the eccentric error is composed of two parts,one is a cosine in circumference and proportional to the eccentric departure factor,and the other is a much smaller quadric factor of the *** the ring is complete,the first error factor is zero because it is integrated in whole ring, but if some part of the ring is blocked,the first factor will be the main *** verifies the result of the a- *** last,a compensation method to the error when some part of the ring is lost is presented based on neural *** results of experiment show that the compensation will make the absolute maximum error descend to half,and the standard deviation of error descends to 1/3.

关键词：补偿作用旋转对称激光三角测量法偏心因子视力测量神经网络

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Error factors in the rotational symmetric triangulation sensor

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Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2007年第SUPPL. 3期28卷 9-13页

作者： Wang, Lei Gao, Jun Wang, Xiaojia Eckstein, Johannes Ott, Peter Lab. of Image Information Processing School of Computer and Information Hefei University of Technology Hefei 230009 China Hochschule Heilbronn Heilbronn D-74081 Germany Dept. of Mechanical and Electrical Engineering Xiamen University Xiamen 361005 China

Rotationally symmetric triangulation (RST) sensor has more flexibility and less uncertainty than traditional laser triangulation because of the abaxial rotationally symmetric optical system. Error and uncertain factors in this kind of sensors are studied in this paper, especially the system uncertainty factors from the error of manufacturing and adjustment. Geometric error model of RST which met the Scheimpflug condition is presented. Based on this model, it is shown that an incident laser spot which departures the rotational axis of the optical system will influent the measurement result quite a lot, and also a tilt detector and optical system aberration will have a smaller effect. At last, Simulation and experiment results are given to verify the analysis.

关键词： Error factors Laser triangulation sensor Optical measurement Rotational symmetric

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

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Asian Journal of Control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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SYSTEMS AND computerS IN JAPAN 1995年第11期26卷 100-109页

作者： WAKIMOTO, K TANAKA, S MAEDA, A SHIMA, M Member Computer and Information Systems Laboratory Mitsubishi Electric Corporation Kamakura Japan 247 Members ALira Maeda:received his M.S. degree in Electrical Engineering from Osaka University and joined Mitsubishi Electric Corporation in 1971. He has been with Information Systems Research Lab. since. He has been working on image information processing systems among others. He is a member of the Information Processing Society. Nonmember

An efficient method to retrieve necessary information from drawings for plant diagrams, logic circuit diagrams, etc. stored in a database is presented. In the image information retrieval systems currently in use, a keyword is assigned to each diagram and information is retrieved by the user by giving the keyword. When the features of the desired image are not registered as a keyword, however, its retrieval becomes difficult. In this paper a method is proposed in which the user shows features of the desired diagram using a sample diagram and a section similar to the sample diagram is retrieved. In this research, diagrams consisting mainly of symbols and connecting lines are considered. This kind of diagram is represented by a graph and similarities in topological features between the sample graph and graph representations of the diagrams in the database are evaluated. The retrieval is then performed based on the value obtained. The results of an experiment conducted using plant diagrams show that similar diagrams can be retrieved using this method.

关键词： image DATABASES MATCHING SIMILARITY RETRIEVAL PLANT DIAGRAMS

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