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CONTROLLING A computer VIA FACIAL ASPECT

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1995年第4期25卷 669-677页

作者： BALLARD, P STOCKMAN, GC Pattern Recognition & Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

Control of a computer workstation via face position and facial gesturing would be an important advance for people with hand or body disabilities as well as for all users. Steps toward realization of such a system are reported here. A computer system has been developed to track the eyes and the nose of a subject and to compute the direction of the face. Face direction and movement is then used to control the cursor. Test results show that the resulting system is usable, although several improvements are needed.

关键词： Hand Workstations Nose Eye computer systems Persons

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Page segmentation using texture discrimination masks

Page segmentation using texture discrimination masks

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IEEE International Conference on image processing

作者： A.K. Jain Y. Zhong Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

We propose a new texture-based page segmentation algorithm which automatically extracts the text, halftone, and line-drawing regions from input greyscale document images. This approach utilizes a neural network to train a set of masks which is optimal for discriminating the three main texture classes in the page segmentation problem: halftone, background, and text and line-drawing regions. The test and line-drawing regions are further discriminated based on connectivity analysis. We have applied the algorithm to successfully segment English and Chinese document images. We also demonstrate that the masks can perform language separation (English/Chinese) when appropriately trained.

关键词： image segmentation Neural networks Gabor filters Natural languages image analysis Multi-layer neural network image texture analysis pattern recognition image processing Laboratories

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Development and application of MCAL tools for marine radar instruction

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computer Applications in Engineering Education 1995年第4期3卷 259-259页

作者： Guirguis, S. Korany, E. Abdel-Bary, A. Institute of Graduate Studies and Research Alexandria University Alexandria Egypt Shawkat K. Guirguis:obtained the BSc and M.Sc. degrees in Computer Science and Automatic Control Faculty of Engineering Alexandria University in 1981 and 1984 respectively. In 1988 he obtained a PhD degree in Electronics and Communications Faculty of Engineering Cairo University co-supervised by the Imperial College of Science and Technology University of London where he spent two years as an academic visitor. His current research interests include software quality management multimedia automatic programming and decision support systems. He is currently a lecturer of Computer Science at the Institute of Graduate Studies & Research Alexandria University. Ezzat A. Korany:received the BS degree in Electrical Engineering (Electronics Section) from the University of Alexandria Egypt in 1971 the MS degree in Electrical Engineering (Computer Section) from Ain Shams University Cairo Egypt in 1977 and the PhD degree in Electrical Engineering (Digital Systems) from Florida Institute of Technology U.S.A. in 1982. He is currently an associate professor of Computer Science at the Institute of Graduate Studies & Research University of Alexandria. His research interests are in data communications and multimedia applications computer networks image processing and pattern recognition. Abdel-Latif A. Abdel-Bary:obtained the BEng degree in Electronics Technology from Arab Maritime Transport Academy in 1986. In 1995 he obtained the MSc degree in Information Technology from the Institute of Graduate Studies & Research Alexandria University. His current research interests include ICAL hypermedia and automatic programming.

In this article a multimedia computer-assisted learning (MCAL) system is presented. The major objective of this work was to investigate the potential of using such systems as tools for transferring instructional course information through various types of computer media as opposed to the classic CAL systems. The philosophy and techniques employed to design the system are investigated. Usage of the implemented system and its merits have been illustrated through its application to teach engineering students and technicians the theory and concepts of marine radar. System design, implementation, test, and revision phases are presented and discussed.

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A general framework for machine vision: Hierarchical token grouping

A general framework for machine vision: Hierarchical token g...

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1994 International Symposium on Speech, image processing and Neural Networks, ISSIPNN 1994

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

ISBN: (纸本)078031865X

While the view of constructive and hierarchical vision prevails, the issues of cooperation and competition among individual modules become crucial. These issues are directly related to one of the most important aspects in computer vision research: integration. A major source of difficulty in developing a consistent and systematic integration formalism is the heterogeneity existing in modules, in information, and in knowledge. The author exploits, using the central theme of grouping, the homogeneous characteristics in vision problem solving and proposes a general framework, called hierarchical token grouping, that facilitates vision problem solving by providing a consistent and systematic environment for integrating modules, cues, and knowledge, all in a globally coherent mechanism. © 1994 IEEE.

关键词： computer vision

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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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IEEE International Conference on image processing

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

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.< >

关键词： image recognition Blood vessels Biomedical imaging Object oriented modeling Blood flow Stochastic processes Solid modeling Data visualization Shape image segmentation

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A general framework for machine vision: hierarchical token grouping

A general framework for machine vision: hierarchical token g...

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International Symposium on Speech, image processing and Neural Networks

作者： Qian Huang Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East Lansing MI USA

ISBN: (纸本)078031865X

关键词： Machine vision Problem-solving computer vision Organizing pattern recognition image processing Laboratories computer science Gratings Humans

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Generalized stochastic tube model: Tracking 3D blood vessels in MR images 12

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

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

作者： Huang, Qian Stockman, George 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

ISBN: (纸本)0818662700

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 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. © 1994 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Blood vessels

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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

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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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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