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International Conference on Document Analysis and recognition

作者： U. Pal N. Sharma T. Wakabayashi F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Tsu Japan

ISBN: (纸本)9780769528229;0769528228

India is a multi-lingual multi-script country but there is not much work towards handwritten character recognition of Indian languages. In this paper we propose a modified quadratic classifier based scheme towards the recognition of off-line handwritten numerals of six popular Indian scripts. Here we consider Devnagari, Bangla, Telugu, Oriya, Kannada and Tamil scripts for our experiment. The features used in the classifier are obtained from the directional information of the numerals. For feature computation, the bounding box of a numeral is segmented into blocks and the directional features are computed in each of the blocks. These blocks are then down sampled by a Gaussian filter and the features obtained from the down sampled blocks are fed to a modified quadratic classifier for recognition. Here we have used two sets of feature. We have used 64 dimensional features for high-speed recognition and 400 dimensional features for high-accuracy recognition in our proposed system. A five-fold cross validation technique has been used for result computation and we obtained 99.56%, 98.99%, 99.37%, 98.40%, 98.71% and 98.51% accuracy from Devnagari, Bangla, Telugu, Oriya, Kannada, and Tamil scripts, respectively.

关键词： Handwriting recognition Natural languages Character recognition Automation pattern recognition Filters Sorting computer vision Feature extraction Writing

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Features selection of cotton disease leaves image based on fuzzy feature selection techniques

Features selection of cotton disease leaves image based on f...

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International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Yan-Cheng Zhang Han-Ping Mao Bo Hu Ming-Xi Li Jiangsu Provincial Key Laboratory of modern Agricultural Equipment and technology Jiangsu University Zhenjiang China College of Engineering and Technology Yunnan Agricultural University Kunming China Department of Electronic Information & Control Engineering Guangxi University of Technology Liuzhou China Huangshi Institute of Technology Huangshi China

In the research of identifying and diagnosing cotton disease using computer vision intellectively in the agriculture, feature selection is a key question in pattern recognition and affects the design and performance of the classifier. In this paper, the fuzzy feature selection approach - fuzzy curves (FC) and surfaces (FS) - is proposed to select features of cotton disease leaves image. In order to get best information for diagnosing and identifying, a subset of independent significant features is identified exploiting the fuzzy feature selection approach. Firstly, utilize FC to automatically and quickly isolate a small set of significant features from the set of original features according to their significance and eliminate spurious features; then, use FS to get rid of the features dependent on the significant features. This approach reduces the dimensionality of the feature space so that lead to a simplified classification scheme appropriate for practical classification applications. The results show that the effectiveness of features selected by the FC and FS method is much better than that selected by human randomly or other methods.

关键词： Cotton Diseases pattern recognition Crops Surface morphology Machine vision Fuzzy sets Wavelet analysis pattern analysis

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vision based gesture recognition for human-robot symbiosis

Vision based gesture recognition for human-robot symbiosis

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Al-Amin Bhuiyan Md. Ezharul Islam Nasima Begum Md. Hasanuzzaman Chang Hong Liu Haruki Ueno Jahangirnagar University Dhaka Bangladesh University of Dhaka Dhaka Bangladesh University of Hull Hull UK National Institute of Information and Communications Technology Tokyo Japan National Institute of Informatics Tokyo Japan

This paper presents a vision based gesture recognition system for human-robot symbiosis. The system is based on the visual information of the face and is commenced with the recognition of face gesture by connected component analysis of the skin color segmentation of images in HSV color model and neural network based pattern-matching strategies. On gesture recognition, robot is being instructed to perform certain tasks by issuing commands. The system is capable of recognizing static gestures comprised of the face poses, and dynamic gestures of face in motion. The effectiveness of the system has been justified over some experiments. The system has been demonstrated with an entertainment robot named ldquoAIBOrdquo as a human-robot symbiotic relationship.

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Two-dimensional shape recognition with Cosine Transform-Based Synthesized Affine Invariant Function

Two-dimensional shape recognition with Cosine Transform-Base...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chun-Hsiung Fang Wei-Song Lin Department of Electrical Engineering National Taiwan University Taipei Taiwan

Two dimensional shape recognition needs to remove the effect of geometric transformations from the contour pattern. This is usually obtained by transforming the contour pattern into an affine invariant form. In this paper, the cosine transform is used to decompose the contour pattern of the object shape into a set of synthesized feature signals (SFS). Then the SFSs are used to evaluate the cosine transform based synthesized affine invariant function (CSAIF) for the shape recognition. The information loss of representing the contour pattern with the CSAIF is minimized by simply neglecting the translation component. This assures high accuracy in recognizing the object shape by discriminating its CSAIF from others. Experiments examine the performance and robustness of the CSAIF based shape recognition and the results are compared with that of using the Fourier affine invariant function.

关键词： Shape Signal synthesis Robustness pattern recognition Object recognition Cameras Wavelet transforms computer vision

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Annotated Databases for the recognition of Screen-Rendered Text

Annotated Databases for the Recognition of Screen-Rendered T...

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International Conference on Document Analysis and recognition

作者： S. Wachenfeld H.-U. Klein X. Jiang Computer Vision and Pattern Recognition Group Department of Computer Science University of Münster Germany

The recognition of screen-rendered text is a novel task. It is performed e.g. by translation tools which allow users to click on any text on the screen and give a translation. Also some commercial OCR programs start to address the problem of reading screenshots. Optical character recognition on screen-shot images can be very challenging due to very small and smoothed fonts. In order to build and compare recognition approaches for screen-rendered text, the availability of standard databases is a fundamental prerequisite. In this paper two freely available databases are presented, one that consists of annotated screenshot images of 28080 single characters and another holding 400 words extracted from documents plus 2 400 generated isolated words. Both databases include meta-information such as x-height, font type, style and rendering conditions. At the example of a developed recognition system, it is shown how these databases can serve for training, testing and optimization.

关键词： Text recognition Image databases Optical character recognition software System testing pattern recognition Rendering (computer graphics) Operating systems Character recognition Character generation Application software

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A ROBOTIC SYSTEM FOR PCBS INSPECTION BASED ON computer vision AND MOBILE PROBES

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IFAC Proceedings Volumes 2007年第3期40卷 171-176页

作者： J. Gómez J. Gámez A.G. González L. Nieto S. Satorres A. Sanchez System Engineering and Automation Department Jaen University Campus las Lagunillas s/n Jaén Spain.

In this paper a new robotized inspection system for PCBs is presented. Electrical and optical tests are carried out by the system, using four mobile probes where two micro CCD cameras are mounted. This new approach becomes a costless and quick setup alternative to the currently used spiked beds for those applications of prototyping developments where maximum flexibility is demanded for the inspection system. A computer vision system provides the capacity for optical inspections where visual tests, like part presence/absence or polarity, can be performed.

关键词： Robotics Automatic Testing Position Control Visual pattern recognition

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Teamwork recognition of embodied agents with hidden Markov models

Teamwork recognition of embodied agents with hidden Markov m...

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IEEE International Conference on Intelligent computer Communication and Processing (ICCP)

作者： Linus J. Luotsinen Hans Fernlund Ladislau Boloni School of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

Recognizing and annotating the occurrence of team actions in observations of embodied agents has applications in surveillance or in training of military or sport teams. We describe the team actions through a spatio-temporal correlated pattern of movement, which can be modeled by a hidden Markov model. The hand-crafting of these models is a difficult task of knowledge engineering, even in application domains where explicit, natural language descriptions of the team actions are available. The main contribution of this paper is an approach through which the library of HMM representations can be acquired from a small number of hand annotated, representative samples of the specific movement patterns. A series of experiments, performed on a dataset describing a real-world terrestrial warfare exercise validates our method and shows good recognition accuracy even in the presence of noisy data. The speed of the recognition engine is sufficiently fast to allow real time annotation of incoming observations.

关键词： Teamwork Hidden Markov models Event detection pattern recognition computer vision Video recording Layout Video sequences Object detection Surveillance

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Perceptual Surface Roughness Classification of 3D Textures Using Support Vector Machines

Perceptual Surface Roughness Classification of 3D Textures U...

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IEEE International Workshop on Haptic, Audio and Visual Environments and Their Applications

作者： Troy L. McDaniel Sethuraman Panchanathan Center for Cognitive Ubiquitous Computing Department of Computer Science and Engineering Arizona State University Tempe AZ USA

Perceptual surface roughness classification describes how a surface's texture feels haptically in terms of perceptual categories such as smooth, rough, bumpy, etc. computer vision and pattern recognition algorithms which estimate a surface's perceptual roughness have a wide range of application areas including robotics, assistive devices, telesurgery and teleperception. In this paper, we propose a novel approach to perceptual surface roughness classification that, unlike previous approaches, is designed to handle multiple roughness categories within the same image. The steps of our approach include (1) texton extraction and classification using a multi-class, non-linear Support Vector Machine; (2) segmentation using the Iterated Conditional Modes algorithm; and (3) overall perceptual roughness classification using a Nearest Neighbor classifier. The proposed approach is evaluated using visio-haptic subjective measures of roughness on images of the 3D texture of real world objects.

关键词： Rough surfaces Surface roughness Surface texture Support vector machines Support vector machine classification computer vision pattern recognition Application software Robot vision systems Image segmentation

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A Decoupled Algorithm for vision Parameter Estimation with Application to the Trifocal Tensor

A Decoupled Algorithm for Vision Parameter Estimation with A...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Tony Scoleri Wojciech Chojnacki Michael J. Brooks Defence Science and Technology Organisation Edinburgh SA Australia School of Computer Science University of Adelaide Adelaide SA Australia

ISBN: (纸本)9781424431618;9780769530673

We consider the problem of estimating parameters of a model described by a system of equations which underlies a wide class of computer vision applications. One method to solve such a problem is the fundamental numerical scheme (FNS) previously proposed by some of the authors. In this paper, a more stable version of FNS is developed, with better convergence properties than the original version. The improvement in performance is achieved by reducing the original estimation problem to a couple of problems of lower dimension. By way of example, the new algorithm has been applied to the problem of estimating the trifocal tensor relating three views of a scene. Experiments carried out with both synthetic and real images reveal the new estimator to be more stable compared to the original FNS method, and commensurate in accuracy with the Gold Standard maximum likelihood estimator.

关键词： Parameter estimation Tensile stress computer vision Application software Maximum likelihood estimation Australia Equations Convergence Phase estimation Digital images

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Automation of Document Editing: A vision Based Approach

Automation of Document Editing: A Vision Based Approach

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International Conference on Computational Intelligence and Multimedia Applications

作者： Jyothi B. Kiranagi Bapu B. Kiranagi D.S. Guru Department of Studies in Computer Science University of Mysore Manasagangotri-Mysore Mysore India

ISBN: (纸本)9780769530505;0769530508

In this paper, a method automating the task of document editing is proposed. The methodology involves a new algorithm to recognize hand written symbols and their positions in printed document. Further, a software package is developed to automate the task of incorporating the suggested corrections (on a hard copy) into the soft copy. The package is develop using pattern recognition and image processing techniques.

关键词： Automation Image segmentation Image processing Feature extraction pattern recognition Visual BASIC Computational intelligence Application software computer science Software packages

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