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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Dominik Rueß Kristian Manthey Ralf Reulke German AeroSpace Center Optical Information Systems Institute of Robotics and Mechatronics Berlin Germany Department of Computer Science Research and Lecture Group Computer Vision Humboldt-Universität zu Berlin Berlin Germany

A typical video surveillance system consists of at least one camera, controlled by an operator. To decrease the human error rate and to generally lessen the burden of operators, many object tracking systems have been implemented, most of which work in 2D image space. If used centralized, this is a very expensive task. Furthermore, if several views are to be fused, large inaccuracies arise due to ground plane assumptions, for instance. Lastly, in outdoor setups, quite often there is a need for slower channels like Wireless LAN which cannot cope with the full resolution data stream. We provide a smart camera system which performs the intensive tasks like background estimation or feature extraction. A central unit only has to process the received data in feature space, increasing scalability. Additionally, the object tracking problem is converted to an accurate 3D feature tracking, avoiding difficulties such as proper object segmentation and adding increased trajectory accuracy. The feature regions are computed within the smart camera. A wide-baseline feature matching approach has been employed to allow more freedom in the placement of the single smart cameras.

关键词： Trajectory Three dimensional displays Cameras Smart cameras Estimation Image reconstruction Feature extraction

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Retinal vessel tortuosity evaluation via Circular Hough Transform

Retinal vessel tortuosity evaluation via Circular Hough Tran...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Farnoosh Ghadiri HamidReza Pourreza Touka Banaee Morteza Delgir Computer Engineering Department Machine Vision Laboratory Ferdowsi University of Mashhad Iran Computer Engineering Department Machine Vision Laboratory University of Ferdowsi Mashhad Iran Ophthalmic Research Center Khatam-Al-Anbia Hospital Medical science University of Mashhad Iran Computer Science Department Robotics Laboratory Northeastern University USA

Retinal vessel tortuosity has shown to be significantly associated with cardiovascular diseases such as hypertension and diabetes. Despite importance of this field a few techniques have been proposed yet. All previous methods rely on a vessel extraction phase, which its accuracy affects final output and also time consuming. Nobility of presented algorithm is to introduce a method for evaluating retinal vessel tortuosity without any explicit vessel detection. We use the Circular Hough Transform (CHT) based on gradient field of the retinal image. Each vessel curve is detected as a semi-circle by Hough transform and tortuosity of the curve is determined with the help of accumulated value of circle center and its radius. As there are no any specific database for tortuosity evaluation, the algorithm was tasted on database consisting of 40 images, mixture of DRIVE database and images from Khatam-Al-Anbia Hospital consisting of 40 retinal images, of which 20 were tortuous and 20 were non-tortuous. The proposed algorithm can achieve classification rate of 92% along with less computation time in compare of previous methods.

关键词： Transforms Retinal vessels Classification algorithms Biomedical measurements Biomedical imaging Image segmentation

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Mobile robot localization using panoramic vision and combinations of feature region detectors

Mobile robot localization using panoramic vision and combina...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Arnau Ramisa Adriana Tapus Ramon Lopez de Mantaras Ricardo Toledo Artificial Intelligence Research Institute Spain Robotics Research Lab Interaction Lab Department of Computer Science University of Southern California Los Angeles USA UAB Campus Computer Vision Center Spain

This paper presents a vision-based approach for mobile robot localization. The environmental model is topological. The new approach uses a constellation of different types of affine covariant regions to characterize a place. This type of representation permits a reliable and distinctive environment modeling. The performance of the proposed approach is evaluated using a database of panoramic images from different rooms. Additionally, we compare different combinations of complementary feature region detectors to find the one that achieves the best results. Our experimental results show promising results for this new localization method. Additionally, similarly to what happens with single detectors, different combinations exhibit different strengths and weaknesses depending on the situation, suggesting that a context-aware method to combine the different detectors would improve the localization results.

关键词： Mobile robots computer vision Detectors Robot kinematics Robot localization Feature extraction Fingerprint recognition robotics and automation USA Councils Image databases

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

No abstract available

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Reference image computation from cartesian specifications for model-free visual servoing

Reference image computation from cartesian specifications fo...

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IFAC/EURON Symposium on Intelligent Autonomous Vehicles

作者： Silveira, Geraldo Rives, Patrick CenPRA - Renato Archer Research Center DRVC - Robotics and Computer Vision Division CP 6162 Campinas/SP13083-970 Brazil INRIA Sophia Antipolis 2004 Route des Lucioles BP 93 Sophia Antipolis Cedex06902 France

It is proposed a new method to compute, from the current unstructured view, a reference (desired) image for visual servoing. The approach, which is based on Cartesian specifications, is made without moving the robot to the target location as in the teach-by-showing paradigm. In addition, although it is assumed a piecewise planar target, no metric information between the object features is required. Several results demonstrate its validity and usefulness, for example in reconnaissance and surveillance tasks. © 2004 IFAC

关键词： Visual servoing

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Reference image computation from cartesian specifications for model-free visual servoing

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IFAC Proceedings Volumes 2004年第8期37卷 496-501页

作者： Geraldo Silveira Patrick Rives Cen PRA - Renato Archer Research Center DRVC - Robotics and Computer Vision Division CP 6162 13083-970 Campinas/SP Brazil INRIA Sophia Antipolis 2004 Route des Lucioles BP 93 06902 Sophia Antipolis Cedex France

关键词： Visual servoing vision based navigation computer vision epipolar geometry Euclidean homography

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APPROXIMATE ORTHOGONAL DISTANCE REGRESSION METHOD FOR FITTING QUADRIC SURFACES TO RANGE DATA

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PATTERN RECOGNITION LETTERS 1994年第8期15卷 781-796页

作者： CAO, XP SHRIKANDE, N HU, GZ Center for Computer Vision and Robotics Research Department of Computer Science Central Michigan University Mt. Pleasant MI 48859 USA

Fitting surfaces to 3-D data is one of the basic methods of surface description for 3-D vision. Most techniques of surface fitting proposed in the literature are ''least-squares''-based that rarely produce satisfactory results if noise level is very high or if the data points are sampled from a small area. A new approach is presented in this paper that minimizes the mean squared approximate orthogonal distances with linearization using Newton's iteration method. This approach usually yields a good fit and the algorithm is reliable and efficient for real applications. Results are reported for synthetic data and several examples of real range data. Experimental results demonstrate that the approximate orthogonal distance performs better than the least squares based methods.

关键词： RANGE DATA LEAST-SQUARES ORTHOGONAL DISTANCE REGRESSION NEWTONS METHOD SURFACE FITTING

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Determination of object location via decomposition of movement

Determination of object location via decomposition of moveme...

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1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 1992

作者： Cao, Xingping Hu, Gongzhu Shrikhande, N. Center for Computer Vision and Robotics Research Department of Computer Science Central Michigan University Mount PleasantMI48859 United States

ISBN: (纸本)0780307372

A method for the determination of the location of an object is presented in this paper. The method uses correspondences of three or more 3-D points with their 2-D images. The object to be located is initially calibrated in a known fixed position. The actual location of the object is obtained by decomposing the total displacement from its calibrated position into a series of small movements each of which can be obtained by solving linear equations. Result is then improved iteratively. Experiments for synthetic and real objects show that the method is accurate, efficient, and robust. © 1992 Institute of Electrical and Electronics Engineers Inc. All rights reserved.

关键词： Location

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Determination Of Object Location Via Decomposition Of Movement

Determination Of Object Location Via Decomposition Of Moveme...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： Xingping Cao Gongzhu Hu N. Shrikhande Center for Computer Vision and Robotics Research Department of Computer Science Central Michigan University Mount Pleasant MI USA

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Quadric surface fitting for sparse range data

Quadric surface fitting for sparse range data

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IEEE International Conference on Systems, Man and Cybernetics

作者： X. Cao N. Shrikhande Center of Computer Vision and Robotics Research Computer Science Department Central Michigan University Mount Pleasant MI USA

The authors present a systematic comparison of three commonly used least-squares based methods that describes the relationship between noise levels, patch sizes and reliability of surface classification in computer vision. The different methods were tested on several sets of synthetic and real data. Complete sets of quadric surfaces were tested. In each case the standard deviation of the Gaussian noise ranged from 0 to 0.05. Four different patch sizes were tested in each case representing data from the entire surface, half surface, quarter surface and from a small patch of the surface. Similar tests were made for two different types of real range data, a sphere and a cylinder. Both synthetic and real data showed improvement in the results obtained by using the M-estimate method in the case of gross outlying points.< >

关键词： Surface fitting Testing Noise level computer vision Least squares methods Differential equations Robot vision systems computer science Object recognition Singular value decomposition

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