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Discriminant performance of the algebraic features of handwritten character images 12

Discriminant performance of the algebraic features of handwr...

12th IAPR International Conference on pattern recognition - Conference B: pattern recognition and Neural Networks, ICPR 1994

作者： Liu, Ke Huang, Yea-Shuan Suen, Ching Y. Yang, Jing-Yu Liu, Lei-Jian Liu, Ying-Jiang Centre for Pattern Recognition and Machine Intelligence Concordia University Suite GM-606 1455 de Maisonneuve Blvd. West MontrealQCH3G 1M8 Canada Department of Computer Science Nanjing University of Science and Technology Nanjing210014 China

ISBN: (纸本)0818662700

One of the most important topics in handwritten character recognition is the extraction of features from character images. In this paper, an algebraic feature extraction technique is applied to recognize handwritten characters. The discriminant performance of the algebraic features extracted from both handprinted characters and totally unconstrained handwritten numerals is studied. Experimental results are provided. © 1994 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Extraction

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Model-based automatic recognition of blood vessels from MR images and its 3D visualization

Model-based automatic recognition of blood vessels from MR i...

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The 1994 1st IEEE International Conference on Image Processing

作者： Huang, Qian Stockman, G.C. IBM Research Division Almaden Research Center 650 Harry Road San JoseCA95120-6099 United States Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

A model-based approach is used for recognizing arterial blood vessels from MRA volumetric data. The modeling includes (1) a generalized stochastic tube model characterizing the structural properties of the vessels, and (2) a bivariate Gaussian function, modeling the expected cross sectional blood flow. This integrated model renders the recognition problem as a parameter estimation problem which is subsequently solved in a hierarchical fashion. Due to the descriptive representation for objects, a new visualization scheme for blood vessels is proposed that allows the observation of the blood flow of each cross section along a recognized vessel. Some experimental results from both synthetic data and real MRA volumes are given. The visualization of interior blood flow patterns for some vessels are also shown. © 1994 IEEE.

关键词： Blood vessels

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Generalized stochastic tube model: tracking 3D blood vessels in MR images

Generalized stochastic tube model: tracking 3D blood vessels...

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International Conference on pattern recognition

作者： Qian Huang G.C. Stockman Almaden Research Center San Jose CA USA Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension of our previously proposed (1993) generalized tube (GT) model. Transitions among adjacent tubes are explicitly parameterized. Integrated with a bivariate Gaussian density function adopted to model the blood flow within cross sections, the GST model is applied to tracking blood vessels in MRA volumetric data. Experimental results on both synthetic data with different degrees of Gaussian noise and real MRA data demonstrated that simultaneously utilizing both models yields robust performance under noisy conditions.

关键词： Stochastic processes Blood vessels Biomedical imaging Shape Blood flow Gaussian noise Image recognition Solid modeling pattern recognition Density functional theory

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VLSI arrays for speech processing with linear predictive coding

VLSI arrays for speech processing with linear predictive cod...

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International Conference on pattern recognition

作者： Y.Y. Tang Tao Li C.Y. Suen Institute of Electronic Information Engineering Chongqing University Chongqing China Department of Computer Science Concordia University Montreal West QUE Canada Centre for Pattern Recognition and Machine Intelligence

The covariance analysis of linear predictive coding has wide applications, especially in speech recognition and speech signal processing. Real-time applications demand very high processing speed for linear predictive coding analysis. VLSI technology which possesses properties of low-cost, high-speed and massive computing capabilities is a suitable candidate. In this paper, systolic array processors for the covariance analysis of linear predictive coding are developed. The covariance analysis of linear predictive coding contains a large set of irregular and nested recurrence equations. Systolizing the algorithm is a difficult task for such a complex problem, Existing methods of systematic design for systolic arrays are not much helpful to this problem. To overcome it, a break-combination method is presented in this paper. In this manner, the task is first decomposed and then mapped onto several interconnected systolic arrays. The resulting systolic arrays of the sub problems are then combined to form a complete solution.

关键词： Very large scale integration Speech processing Linear predictive coding Systolic arrays Speech analysis Signal analysis Speech recognition Speech coding Array signal processing Difference equations

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Combining Feature Extraction and Hidden Markov Modelling for Automatic Speech recognition

Combining Feature Extraction and Hidden Markov Modelling for...

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International Conference on Communication Technology(ICCT’94)

作者： WU Jianxiong CHEN Peifang CHAN Chorkin DENG Li Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Department of Computer Science University of Hong Kong Dcpartment of Electrical and Computer Engineering University of WaterlooWaterlooOntarioCanada N2L 3G1.

＜正＞Feature extraction is very important for the classifier design and the overall performance of *** recognition ***,due to the lack of theoretical guidances,feature extraction and classifier design are usually treated separately in current speech recognition *** *** proposes an approach to combine linear feature extraction with continuous density hidden Markov modeling（HMM） which is currently the most successful speech pattern classifier.A maximumlikelihood based algorithm is derived to iteratively train HMM parameters as well as the parameters of the feature *** algorithm is an exteusion of the Baum-Welcli parameter re-estimation algorithm for conventional HMMs and thus has a nice property of guara, nteed convergence.

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Recognizing elongated objects using invariant surface features and matched filters 2

Recognizing elongated objects using invariant surface featur...

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Mathematical Methods in Medical Imaging II 1993

作者： Huang, Qian Stockman, George C. Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

Many biological objects are elongated. This research addresses the issue of recognizing elongated objects from both 2D intensity images and 3D volumes. A mathematical model, called tube model, is developed for this class of objects and is effectively utilized in two stages of recognition. The explicit relationships between geometrical surface features and the object model parameters are quantitatively exploited to automatically locate seeds for recognition. Invariant surface features are used to constrain or hypothesize the objects of interest. The verification of a hypothesis is performed by correlating a matched filter, dynamically generated based on the hypothesis, with the sensor data. The tubes identified in such a local recognition process serve as the seeds from which the global recognition process is initiated. Each seed is swept along the trajectory where the best-fit is found. A smooth sweep is controlled by a set of adaptive constraints computed dynamically from an on-line sweeping history. We apply the proposed method to real world data from different application domains. Experimental results are presented and discussed. © 1993 SPIE. All rights reserved.

关键词： Matched filters

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Generalized tube model: recognizing 3D elongated objects from 2D intensity images

Generalized tube model: recognizing 3D elongated objects fro...

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Conference on computer Vision and pattern recognition (CVPR)

作者： Q. Huang G.C. Stockman Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

The issue of recognizing 3D elongated objects from 2D intensity images is addressed. A tube model, locally similar to generalized cones, is developed for the class of elongated objects. A recognition strategy that combines 2D contour properties and surface shading information is used to exploit the power of the 3D model. Reliable contours provide constraints for localizing the objects of interest. The theory of optimal filters is adopted in verifying the shading of hypothesized objects. Object recognition is achieved through optimizing the signal-to-noise response with respect to model parameters. A sweeping operation is proposed as a further stage of identifying objects so that the overall performance of the system does not heavily rely on the quality of local feature detection.< >

关键词： Image recognition Filters Object recognition Object detection Robustness Image processing laboratories computer science Filtering theory Power system modeling

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Performance evaluation of nonlinear shape normalization methods for the recognition of large-set handwritten characters

Performance evaluation of nonlinear shape normalization meth...

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International Conference on Document Analysis and recognition

作者： S.-W. Lee J.-S. Park Y. Tang Department of Computer science Chungbuk National University Cheonghu Chungbuk South Korea Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal QUE Canada

Recently, several nonlinear shape normalization methods have been proposed in order to compensate for shape distortions in large-set handwritten characters. The authors review these methods from the two points of view: feature projection and feature density equalization. The former makes a feature projection histogram by projecting a certain feature at each point into horizontal- or vertical-axis and the latter equalizes the feature densities of input image by re-sampling the feature projection histogram. Then, a quantitative evaluation for these methods has been made based on the following criteria: recognition rate, processing speed, computational complexity, and degree of variation.< >

关键词： Shape Nonlinear distortion Handwriting recognition Character recognition pattern recognition Histograms computer science Machine intelligence pattern matching Image converters

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A SYSTEMATIC EVALUATION OF SKELETONIZATION ALGORITHMS

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International Journal of pattern recognition and Artificial Intelligence 1993年第5期7卷 1203-1225页

作者： SEONG-WHAN LEE LOUISA LAM CHING Y. SUEN Department of Computer Science Chungbuk National University San 48 Gaeshindong Cheongju Chungbuk 360–763 Republic of Korea Centre for Pattern Recognition and Machine Intelligence GM-606 Concordia University 1455 de Maisonneuve Blvd. West Montreal Quebec H3G 1M8 Canada

As a result of its central role in the preprocessing of image patterns, or because of its intrinsic appeal, the design of skeletonization algorithms has been a very active research area. However, few attempts have been made to evaluate the performance of different skeletonization algorithms. This paper presents the results of experiments to evaluate the performance of 20 skeletonization algorithms previously published in the literature. These algorithms have been implemented on the SUN 3/60 workstation in C and tested with a large variety of character patterns. A systematic comparison of these algorithms has been made based on the following criteria: reconstructibility, computation speed, similarity to the reference skeleton, quality of the skeleton, connectivity after skeletonization, and the degree of parallelism.

关键词： Performance evaluation of skeletonization algorithms skeleton thinning medial axis transform

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Parallel dynamic programming algorithms for image recognition based on its contour 2nd

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2nd International Conference on Parallel Image Analysis, ICPIA 1992

作者： Thang, Giang Vu Department for Pattern Recognition Institute of Computer Science Lie ugiai Tu liem Ba dinh Ha noi Viet Nam

ISBN: (纸本)9783540563464

The dynamic programming IB an important procedure for speech understanding and image recognition. In this paper, the application of dynamic programming to recognize the image basing on its contour is recalled, then the parallelism of this process is analyzed and exploited. Some parallel dynamic programming algorithms for evaluating the smallest value of the minimum intersegment distance path in a data table are designed in detail on several kinds of the specialized parallel computer, such as 2D Systolic Array, ID Systolic Array and an associative computer of the S1MD type. Their computation complexities are respectively estimated as 0(k), 0(k) and 0(kw), while the cost O(nui) is obtained on serial computer, where k=max(2n,m), m is the number of segments of a contour to be recognized, n gives the number of segments of the sample contour and w is the bit-width of the used associative fields. © Springer-Verlag Berlin Heidelberg 1992.

关键词： Dynamic programming

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