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10th IAPR Conference on Machine vision Applications, MVA 2007

作者： Bais, Abdul Sablatnig, Robert Khawaja, Yahya M. Novak, Gregor Institute of Computer Technology Vienna University of Technology Vienna Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation ViennaUniversity of Technology Vienna Austria Department of Electrical Engineering NWFP University of Engineering and Technology Peshawar Pakistan

ISBN: (纸本)9784901122078

This paper presents uncertainty propagation in landmark based position estimation methods. Analysis of two methods has been carried out where robot position is estimated by detecting one or two globally distinct features using a pivoted stereo vision system. We make a basic assumption about error in estimating point features in camera images and propagate it into robot position estimate using first order approximation of non-linear functions. Simulation results illustrate the performance of the method.

关键词： Stereo image processing

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Dynamic omni-directional vision localization using a beacon tracker based on particle filter

Dynamic omni-directional vision localization using a beacon ...

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Conference on Intelligent Robots and computer vision XXV - Algorithms, Techniques, and Active vision

作者： Cao, Zuoliang Liu, Shiyu Tianjin Univ Technol Tianjin 300191 Peoples R China

ISBN: (纸本)9780819469243

Omni-directional vision navigation for AGVs appears definite significant since its advantage of panoramic sight with a single compact visual scene. This unique guidance technique involves target recognition, vision tracking, object positioning, path programming. An algorithm for omni-vision based global localization which utilizes two overhead features as beacon pattern is proposed in this paper. An approach for geometric restoration of omni-vision images has to be considered since an inherent distortion exists. The mapping between image coordinates and physical space parameters of the targets can be obtained by means of the imaging principle on the fisheye lens. The localization of the robot can be achieved by geometric computation. Dynamic localization employs a beacon tracker to follow the landmarks in real time during the arbitrary movement of the vehicle. The coordinate transformation is devised for path programming based on time sequence images analysis. The beacon recognition and tracking are a key procedure for an onmi-vision guided mobile unit. The conventional image processing such as shape decomposition, description, matching and other usually employed technique are not directly applicable in omni-vision. Particle filter (PF) has been shown to be successful for several nonlinear estimation problems. A beacon tracker based on Particle Filter which offers a probabilistic framework for dynamic state estimation in visual tracking has been developed. We independently use two Particle Filters to track double landmarks but a composite algorithm on multiple objects tracking conducts for vehicle localization. We have implemented the tracking and localization system and demonstrated the relevant of the algorithm.

关键词： omni-directional vision dynamic localization beacon tracker particle filter

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A geometry-based local descriptor for range data

A geometry-based local descriptor for range data

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Australian pattern recognition Society (APRS)

作者： Viksten, Fredrik Nordberg, Klas Image Coding Group Dept. of Electrical Engineering Linköping University Sweden Computer Vision Laboratory Dept. of Electrical Engineering Linköping University Sweden

ISBN: (纸本)0769530672

We present a novel local descriptor for range data that can describe one or more planes or lines in a local region. It is possible to recover the geometry of the described local region and extract the size, position and orientation of each local plane or line-like structure from the descriptor. This gives the descriptor a property that other popular local descriptors for range data, such as spin images or point signatures, does not have. The estimation of the descriptor is dependant on estimation of surface normals but does not depend on the specific normal estimation method used. It is shown that is possible to extract how many planar surface regions the descriptor represents and that this could be used as a point-of-interest detector. © 2007 IEEE.

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Detecting motion patterns via direction maps with application to surveillance

Detecting motion patterns via direction maps with applicatio...

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7th IEEE Workshop on Applications of computer vision, WACV 2005

作者： Gryn, Jacob M. Wildes, Richard P. Tsotsos, John K. Computer Science and Centre for Vision Research York University Toronto Ont. M3J 1P3

ISBN: (纸本)0769522718

To facilitate accurate and efficient detection of motion patterns in video data, it is desirable to abstract from pixel intensity values to representations that explicitly and compactly capture movement across space and time. For example, in the monitoring of surveillance video, it is useful to capture movement, as potential targets of interest traverse the scene in specific ways. Toward such ends, the "direction map" is introduced: a novel representation that captures the spatiotemporal distribution of direction of motion across regions of interest in space and time. Methods are presented for recovering direction maps from video, constructing direction map templates to define target patterns of interest and comparing predefined templates to newly acquired video for pattern detection and localization. The approach has been implemented with real-time considerations and tested on over 6300 frames across seven surveillance videos. Results show an overall recognition rate of approximately 90% hits vs. 7% false positives.

关键词： pattern recognition

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pattern Theory : From Representation to Inference illustrated edition

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2007年

作者： Ulf Grenander Michael I. Miller

ISBN: (纸本)9780198505709;9780199297061;9780191523113

pattern Theory: From Representation to Inference provides a comprehensive and accessible overview of the modern challenges in signal, data and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision. Aimed at graduate students in biomedical engineering, mathematics, computer science and electrical engineering with a good background in mathematics and probability, the text includes numerous exercises and an extensive bibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion website. The book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via conditioning structure and Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn, and Chapters 10, 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in the newly emergent field of Computational Anatomy, and finally Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.

关键词： pattern recognition systems pattern perception

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Proceeding of the IEEE computer Scoeity Conference on computer vision and pattern recognition: Preface

Proceedings of the IEEE Computer Society Conference on Compu...

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Proceedings of the IEEE computer Society Conference on computer vision and pattern recognition 2007年

作者： Bhanu, Bir Ratha, Nalini University of California Riverside IBM T. J. Watson Research Center

No abstract available

ISBN: (纸本)1424411807

No abstract available

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pattern recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007 Proceedings

Pattern Recognition and Image Analysis: Third Iberian Confer...

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3rd Iberian Conference on pattern recognition and Image Analysis, IbPRIA 2007

ISBN: (纸本)9783540728481

The proceedings contain 157 papers. The topics discussed include: known unknowns: novelty detection in condition monitoring;seeing the invisible and predicting the unexpected;vision-based SLAM in real-time;handwritten symbol recognition by a boosted blurred shape model with error correction;Bayesian hyperspectral image segmentation with discriminative class learning;comparison of unsupervised band selection methods for hyperspectral imaging;learning mixture models for gender classification based on facial surface normals;motion segmentation from feature trajectories with missing data;segmentation of rigid motion from non-rigid 2D trajectories;hierarchical eyelid and face tracking;automatic learning of conceptual knowledge in image sequences for human behavior interpretation;a comparative study of local descriptors for object category recognition:SIFT vs HMAX;and moment-based pattern representation using shape and grayscale features.

关键词： pattern recognition

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Efficient segmentation based on Eikonal and diffusion equations

Efficient segmentation based on Eikonal and diffusion equati...

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18th International Conference on pattern recognition (ICPR 2006)

作者： Alvino, Christopher Unal, Gozde Slabaugh, Greg Peny, Bertrand Fang, Tong Siemens Corp Res Intelligent Vis & Reasoning Princeton NJ 08540 USA

Segmentation of regions of interest in an image has important applications in medical image analysis, particularly in computer aided diagnosis. Segmentation can enable further quantitative analysis of anatomical structures. We present efficient image segmentation schemes based on the solution of distinct partial differential equations (PDEs). For each known image region, a PDE is solved, the solution of which locally represents the weighted distance from a region known to have a certain segmentation label. To achieve this goal, we propose the use of two separate PDEs, the Eikonal equation and a diffusion equation. In each method, the segmentation labels are obtained by a competition criterion between the solutions to the PDEs corresponding to each region. We discuss how each method applies the concept of information propagation from the labelled image regions to the unknown image regions. Experimental results are presented on magnetic resonance, computed tomography, and ultrasound images and for both two-region and multi-region segmentation problems. These results demonstrate the high level of efficiency as well as the accuracy of the proposed methods.

关键词： computer vision image processing pattern recognition variational methods segmentation

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Learning and modeling biosignatures from tissue images

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computerS IN BIOLOGY AND MEDICINE 2007年第11期37卷 1539-1552页

作者： Gilfeather, Frank Hamine, Vikas Helman, Paul Hutt, Julie Loring, Terry Lyons, C. Rick Veroff, Robert Univ New Mexico Albuquerque NM 87131 USA Lovelace Resp Res Inst Albuquerque NM 87108 USA

Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and naive mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： automated disease classification computational biology statistical models of infection computer vision feature extraction pattern recognition machine learning Bayesian networks

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Design and simulation of the positioning system using computer vision for IC packaging

Design and simulation of the positioning system using comput...

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8th International Conference on Electronics Packaging Technology

作者： Li, Junlan Wang, Yizhong Zhang, Dawei Zhao, Xingyu Tianjin Univ Dept Mech Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9781424413911

A positioning system using computer vision technologies is designed to obtain high precision during wire bonding. The main processes are on-line examining the position of a chip before bonding, calculating its locating errors, and cooperating with control system to eliminate these errors. Therefore, chips' accurate positions can be obtained which can improve the technology of wire bonding. A novel optical imaging system is designed to get images with high quality. Micro-imaging system consists of two lenses to achieve appropriate magnifying multiple and working distance, which are 4 and distance is 47mm, respectively. Illumination lens provides steady and symmetrical lighting condition on working area. Normalized correlation image registration algorithm is applied as the pattern recognition positioning algorithm of wire bonder's vision system. Some simulation experiments have been carried out, which show that the precision of pattern recognition in this system is 3.5 mu m, which satisfies the requirements of wire bonding.

关键词： Wire

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