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作者： Masood-Ur-Rehman Jiancheng, Fang Saffih, Faycal Wei, Quan School of Instrumentation Science and Opto-electronics Engineering Beijing University of Aeronautics and Astronautics Beijing -100083 China

ISBN: (纸本)9789698741075

Autonomous star trackers are rapidly becoming the most accurate means of attitude determination due to their high accuracy, small size, light weight and simple functionality, making them devices of choice in nearly all modern space vehicles. In all star trackers star centroiding and pattern recognition are fundamental processes, necessary for attitude determination. In this paper an automatic star pattern recognition scheme is presented for currently available high frame rate imagers especially for Active Pixel Sensors (APS) (because of their random access ability) in tracking mode. this scheme uses the knowledge of boresight direction to pick stars from star catalog and convert them in imager's coordinates to predict the regions of star locations in next image frame. By keeping track of the reference catalog stars and centroiding results of the predicted stars regions from image, the pattern recognition is done automatically. the boresight direction is updated at each sequential frame. Simulation results show that the proposed scheme is computationally more efficient and faster as compared to normal centroiding scheme. In addition, it is shown that it can easily handle the problem created by double stars and it inherently decreases the probability of false stars and noisy spikes. © 2009 IEEE.

关键词： Star trackers

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COBVIS-D: A Computer Vision System for Describing the Cephalo-Ocular Behavior of Drivers in a Driving Simulator

COBVIS-D: A Computer Vision System for Describing the Cephal...

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6th international conference on Image Analysis and recognition

作者： Beauchemin, Steven Varcheie, Parisa Darvish Zadeh Gagnon, Langis Laurendeau, Denis Lavalliere, Martin Moszkowicz, thierry Prel, Florent Teasdale, Normand Univ Western Ontario Dept Comp Sci London ON N6A 5B7 Canada Centre rech informat Montreal Montreal PQ H3A 1B9 Canada Univ Laval Dept Elect & Comp Engn Quebec City PQ G1V 0A6 Canada Univ Laval Div kinesiol Quebec City PQ G1K 7P4 Canada

ISBN: (纸本)9783642026102

this paper describes current research combining computer vision, virtual reality and kinesiology for analyzing the cephalo-ocular behavior of drivers in realistic driving contexts. the different components of the system are described and results are provided for each one. the ultimate goal of the system is to achieve automatic analysis of drivers' behavior in order to design training programs tailored to their driving habits.

关键词： computer vision tracking facial expression detection driving simulator virtual reality

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Artery Wall Extraction from Intravascular OCT Images

Artery Wall Extraction from Intravascular OCT Images

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6th international conference on Image Analysis and recognition

作者： Bourezak, Rafik Lamouche, Guy Cheriet, Farida Ecole Polytech Dept Comp Engn Campus Univ Montreal 2500 Chemin Polytech Montreal PQ H3T 1J4 Canada Natl Res Council Canada Ind Math Inst Boucherville PQ J4B 6Y4 Canada Campus Univ Montrea Ecole Polytechn Montreal Dept Engn Phys Montreal H3T 1J4 PQ Canada

ISBN: (纸本)9783642026102

In this article, we present a new method to extract internal and external borders (intimal-adventitial) of arteries from Optical Coherence Tomography (OCT) images. the method is based on A-scan segmentation. First, the distribution of the grey level values on every A-scan is analyzed separately using a sliding window to approximate a single-lobe distribution. Our hypothesis is that the position of the arterial tissue corresponds to the window which exhibits the largest single-lobe distribution. Once all the tissue is extracted from the image, every segmented A-scan position is corrected using a block of neighbouring segmented A-scans. Experimental results show that the proposed method is accurate and robust to extract arterial tissue.

关键词： OCT A-scan segmentation intravascular imaging image segmentation image filtering

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Prostate TRUS Image Region-Based Feature Extraction and Evaluation

Prostate TRUS Image Region-Based Feature Extraction and Eval...

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6th international conference on Image Analysis and recognition

作者： Hui, Eric K. T. Mohamed, S. S. Salama, M. M. A. Fenster, A. Univ Waterloo Waterloo ON N2L 3G1 Canada Univ Western Ontario London ON N6A 3K7 Canada

ISBN: (纸本)9783642026102

In this work a new informative feature set is proposed to identify Suspicious Regions Of Interest (ROIs) in the prostate TransRectal UltraSound (TRUS) images. the proposed features are region based to overcome the limitations present in the pixel based feature extraction methods. First a thresholding algorithm integrated with the medical information is used to identify different candidate ROIs. Next, image registration is performed to transform the prostate image to a model based from which some of the proposed region based features are extracted. Subsequently, the proposed raw based and model based region features are extracted at the region level. Finally Mutual Information is used to evaluate the extracted features and compare their information content with both the typical pixel based features and the well known texture and grey level features. It was found that the proposed features provide more information than both the texture features and the pixel based features.

关键词： feature extraction region-based features mutual information symmetry measure TRUS

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Perceptually Relevant pattern recognition Applied to Cork Quality Detection

Perceptually Relevant Pattern Recognition Applied to Cork Qu...

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6th international conference on Image Analysis and recognition

作者： Paniagua, Beatriz Green, Patrick Chantler, Mike Vega-Rodriguez, Miguel A. Gomez-Pulido, Juan A. Sanchez-Perez, Juan M. Univ Extremadura Escuela Politecn Dept Technol Comp & Commun Campus Univ S-N Caceres 10071 Spain Heriot Watt Univ Sch Life Sci Edinburgh EH14 4AS Midlothian Scotland Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9783642026102

this paper demonstrates significant improvement in the performance of a computer vision system by incorporating the results of an experiment oil human visual perception. this system was designed to solve a problem existing in the cork industry: the automatic classification of cork samples according to their quality. this is a difficult problem because cork is a natural and heterogeneous material. An eye-tracker was used to analyze the gaze patterns of a human expert trained in cork classification, and the results identified visual features of cork samples used by the expert in making decisions. Variations in lightness of the cork surface proved to be a key feature, and this finding was used to select the features included in (lie final system: defects in the sample (thresholding), size of the biggest defect (morphological operations), and four-Laws textural features, all working oil a Neuro-Fuzzy classifier. the results obtained from the final system show lower error rates than previous systems designed for this application.

关键词： Stopper quality cork industry vision science, image processing automated visual inspection system perceptual features eye tracking

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On the performances of neuronal classifiers for pattern recognition

On the performances of neuronal classifiers for pattern reco...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Hayet Boughrara Mohamed Chtourou Research unit on Intelligent Control design and Optimisation of complex Systems (ICOS) Sfax Engineering School University of Sfax Sfax Tunisia

this work consists on the evaluation of the performances of three neural classifiers. the Multi-Layer Perceptron (MLP), the Self-Organizing Map (SOM), the Learning Vector Quantization (LV Q) are considered by this study. the example that will be considered in the evaluation of the technical classifications's performances is the handwritten character recognition.

关键词： pattern recognition Character recognition Performance evaluation Multilayer perceptrons Vector quantization Data mining Intelligent control Signal design Design optimization Design engineering

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Energy Minimization Methods in Computer Vision and pattern recognition: 7th international conference, EMMCVPR 2009 Bonn, Germany, August 24-27, 2009 Proceedings - Preface

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2009年 5681 LNCS卷 VI页

作者： Cremers, Daniel Boykov, Yuri Blake, Andrew Schmidt, Frank R. Universität Bonn Institut für Informatik III Römerstraße 164 53117 Bonn Germany Computer Science Department University of Western Ontario London ON N6A 5A5 Canada Microsoft Research J.J. Thomson Avenue CB3 OFB Cambridge United Kingdom

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Automatic star pattern recognition in tracking mode

Automatic star pattern recognition in tracking mode

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international Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Masood-Ur-Rehman Fang Jiancheng Faycal Saffih Quan Wei School of Instrumentation Science and Optoelectronics Engineering Beijing Aeronautics and Astronautics University Beijing China

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Robust and Accurate recognition of Veins in Fruit Fly Wings

Robust and Accurate Recognition of Veins in Fruit Fly Wings

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international Symposium on Image and Signal Processing and Analysis

作者： Hiroshi Hatsuda Keigo Muramatsu Toshiro Aigaki Shinichi Morishita Department of Computational Biology The University of Tokyo Department of Biological Sciences Tokyo Metropolitan University

ISBN: (纸本)9781424456499

Manual inspection of the morphological differences in the veins of wings in fruit fly mutants is useful for uncovering the relevant genes associated with fundamental biological functions. However, manual morphological inspection is not feasible when processing numerous mutants that were systematically created using fruit fly genome information. Another difficulty is how to process mutant wings that violate the typical vein pattern in the wild-type fruit fly. We require an automatic image recognition system to quantify morphological differences robustly and accurately, but this is a difficult task because exact quantification of significant differences from the typical pattern is highly nontrivial. We propose a robust and accurate image recognition technique that can analyze the veins of fruit fly wings robustly and accurately using a hierarchical model matching algorithm. the results of an experiment with 1000 images demonstrated the feasibility of our technique.

关键词： Robustness Veins Genomics bioinformatics Image analysis Inspection Deformable models Signal processing algorithms Biology Image recognition

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Texture classification of woven fabric based on a GLCM method and using multiclass support vector machine

Texture classification of woven fabric based on a GLCM metho...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Yassine Ben Salem Salem Nasri Networks & Communication Systems Unit Ecole dingénieurs de Monastir Monastir Tunisia

this paper proposes the classification of three mean woven textile fabrics. the classifier is based on the texture analysis of woven fabric images for the recognition. In the pattern recognition phase, the co-occurrence matrix is applied to calculate the texture characteristics, such as the angular second moment, the correlation, the homogeneity and the contrast. We have varying the offset in distance and orientation, the best offset was retained for the classification. Taking advantage of the difference between the woven fabric textures of these parameters, a support vector machine is adopted as a classifier to categorize the type of woven fabric. Two types of multi-class classifiers were tested: the one against all and the one against one. the experimental results show that some of the studied parameters are more compatible with the SVM classifier than the others, for example the homogeneity leads to a recognition of 100% which makes it the most compatible parameter of the feature texture for the classification. We note that the one against all classifier gives better results than the other.

关键词： Fabrics Support vector machines Support vector machine classification pattern recognition Image analysis Image texture analysis pattern analysis Shape Digital filters Filtering

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