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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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A sequential method of extracting contour chains from an image

A sequential method of extracting contour chains from an ima...

引用

international conference on pattern recognition

作者： N.W. Strathy F. Said C.Y. Suen Centre for Pattern Recognition & Machine Intelligence Concordia Univ. Montreal Que. Canada Centre for Pat. Rec. and Machine Intel GM-606 Concordia University Montreal QUE Canada

An efficient sequential method of tracing contour chains around foreground objects in an image is presented. the image is scanned row by row, extracting the run lengths of each row, determining their connectivity with runs in the preceding row, and incrementally building up contour chains accordingly. the contours are stored in a new list structure that reflects their topological nesting in the image. the organization of this list provides a greater degree of topological information than is provided by other methods.

关键词： Pixel data mining System testing Image segmentation data structures Clocks Merging

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A genetic learning system for on-line character recognition

A genetic learning system for on-line character recognition

引用

international conference on pattern recognition

作者： B. Bontempi A. Marcelli Dipartimento di Informatica e Sistemistica UniversitaE28099 di Napoli Federico II Napoli Italy

In this paper we present the result of an investigation on a new method to perform online character recognition. the method is based on a genetic algorithm used as the engine of a learning system to produce prototypes of the characters, and on a string matcher to perform the classification. the learning mechanism, provided by a genetic algorithm, allows the system to have both a writer independent core and an adaptation scheme to finely tune the recognizer to the writer's style. Preliminary experiments have shown that the method is very promising, since it produces prototypes general enough to cope with the large variability encountered when handling specimen produced by different writers. Moreover, it provides a natural and effective writer-dependent learning of new symbols.

关键词： learning systems Character recognition Prototypes Genetic algorithms Information analysis Writing Humans Engines data mining Handwriting recognition

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Vector templates and handprinted digit recognition

Vector templates and handprinted digit recognition

引用

international conference on pattern recognition

作者： J.R. Parker Laboratory for Computer Vision Department of Computer Science University of Calgary Calgary AB Canada

While a multitude of template matching strategies have been applied to printed text recognition, the variation seen in handprinted characters generally reduces the usefulness of this technique. What is suggested in this paper is the use of scalable vector templates, which can be used to generate a template with the same scale and line width attributes as an arbitrary input character image. the best match is the template having the smallest total distance between black pixels. Multiple templates are used for each character and digits only are used as a sample data set.

关键词： Character recognition Humans Shape Laboratories Computer vision Computer science Text recognition Character generation machine learning Skeleton

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Scalable parallel implementations of perceptual grouping on connection machine CM-5

Scalable parallel implementations of perceptual grouping on ...

引用

international conference on pattern recognition

作者： V.E. Prasanna Cho-Li Wang Department of EE-Systems University of Southern California Los Angeles CA USA

Perceptual grouping is a key step in vision to organize image data into structural hypotheses to be used for high level analysis. We propose data allocation and load balancing strategies which reduce the communication cost and evenly distribute the grouping operations among the processors. these techniques result in scalable algorithms for performing perceptual grouping on CM-5. the performance of our algorithms depends only on the total grouping operations generated by the image data and is independent of the distribution of the data among the processors. Our implementations show that given a 1 K/spl times/1 K input image, extraction of line segments and several perceptual grouping steps can be performed in 5.0 seconds using a partition of CM-5 having 32 processing nodes. A serial implementation of these steps on a Sun Sparc 400 takes more than 2 minutes.

关键词： Image edge detection Image segmentation Buildings Phase detection Image analysis Load management Costs Image generation data mining Sun

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Comparison of classifier methods: a case study in handwritten digit recognition

Comparison of classifier methods: a case study in handwritte...

引用

international conference on pattern recognition

作者： L. Bottou C. Cortes J.S. Denker H. Drucker I. Guyon L.D. Jackel Y. LeCun U.A. Muller E. Sackinger P. Simard V. Vapnik Heuristique Paris France AT and T Bell Laboratories Inc. Holmdel NJ USA Electronics Laboratory Swiss Federal Institute of Technology Zurich Switzerland

this paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and memory requirements. When available, we report measurements of the fraction of patterns that must be rejected so that the remaining patterns have misclassification rates less than a given threshold.

关键词： Computer aided software engineering Handwriting recognition Testing NIST databases Training data Time measurement machine learning Laboratories pattern recognition

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Real-time facial-feature tracking based on matching techniques and its applications

Real-time facial-feature tracking based on matching techniqu...

引用

international conference on pattern recognition

作者： H. Sako M. Whitehouse A. Smith A. Sutherland Research & Development Centre Dublin Laboratory (HDL) OE28099Reilly Institute Trinity College Hitachi Europe Limited Dublin Ireland

this paper describes a method of real-time facial-feature extraction which is based on matching techniques. the method is composed of facial-area extraction and mouth-area extraction using colour histogram matching, and eye-area extraction using template matching. By the combination of these methods, we can realize real-time processing, user-independent recognition and tolerance to changes of the environment. Also, this paper touches on neural networks which can extract characteristics for recognizing the shape of facial parts. the methods were implemented in an experimental image processing system, and we discuss the cases that the system is applied to man-machine interface using facial gesture and to sign language translation.

关键词： Shape Feature extraction Image recognition Histograms Neural networks Face recognition data mining User interfaces Samarium Statistics

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ROBUST FEATURE-SELECTION ALGORIthMS

ROBUST FEATURE-SELECTION ALGORITHMS

引用

5th international conference on Tools with Artificial Intelligence (TAI 93)

作者： VAFAIE, H DEJONG, K GEORGE MASON UNIV CTR ARTIFICIAL INTELLIGENCEFAIRFAXVA 22030

ISBN: (纸本)0818642009

Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive control, and machine learning. Our experience with traditional feature selection algorithms in the domain of machine learning lead to an appreciation for their computational efficiency and a concern for their brittleness. this paper describes an alternate approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggest that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in computational efficiency.

关键词： learning systems

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USING NEURAL NETWORKS FOR thE INTERPRETATION OF BIOPROCESS data

USING NEURAL NETWORKS FOR THE INTERPRETATION OF BIOPROCESS D...

引用

2nd IFAC Symposium on Modeling and Control of Biotechnical Processes 1992/5th international conference on Computer Applications in Fermentation Technol

作者： RAJU, GK COONEY, CL MIT DEPT CHEM ENGNCTR BIOTECHNOL PROC ENGNCAMBRIDGEMA 02139

ISBN: (纸本)0080417108

During the course of most bioprocess development programs a large amount of process data is generated and stored. However, while these data records contain important information about the process, little or no use is made of this asset. the work described here uses a neural network approach to `learn' to recognize patterns in fermentation data. Neural networks, trained using fermentation data generated from previous runs, are then used to interpret data from a new fermentation. We propose a task decomposition approach to the problem. the approach involves decomposing the problem of bioprocess data interpretation into specific tasks. Separate neural networks are trained to perform each of these tasks which include fault diagnosis, growth phase determination and metabolic condition evaluation. these trained networks are combined into a multiple neural network hierarchy for the diagnosis of bioprocess data. the methodology is evaluated using experimental data from fed-batch, Saccharomyces cerevisiae fermentations. We argue that the task decomposition approach taken here allows for each network to develop a task specific representation and that this in turn, can lead to network activations and connection weights that are more clearly interpretable. these expert networks can now be pruned to remove nodes that do not contribute significant additional information.

关键词： NEURAL NETWORK TASK DECOMPOSITION SACCHAROMYCES-CEREVISIAE learning, pattern recognition, data INTERPRETATION, BIOPROCESS, MODULAR APPROACH, BIOPROCESS DEVELOPMENT, FED-BATCH

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5th international conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1992

引用

5th international conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1992

ISBN: (纸本)9783540556015

the proceedings contain 77 papers. the special focus in this conference is on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. the topics include: Gaining strategic advantage with real-time distributed artificial intelligence;intelligent databases and interoperability;the TOVE project towards a common-sense model of the enterprise;automatization in the design of image understanding systems;constraint programming — an alternative to expert systems;case-based reasoning in expert system assisting production line design;scaling-up model-based troubleshooting by exploiting design functionalities;application of knowledge-based systems to optimised building maintenance management;application of model-based reasoning to the maintenance of telecommunication networks;advanced information modelling for integrated network management applications;an integration of case-based and model-based reasoning and its application to physical system faults;analysing particle jets with artificial neural networks;convergence behaviour of connectionist models in large scale diagnostic problems;pattern recognition approach to an acoustical quality test of burnt ceramic products;enhancing software engineering capabilities of PROLOG by object-oriented concepts;specifying decision-making processes;operationalizing software reuse as a problem in inductive learning;towards a real cad-system using ai technology;WIZ — a prototype for knowledge-based drawing interpretation;intelligent process control by means of expert systems and machine vision;panter — knowledge based image analysis system for workpiece recognition;using knowledge-based software engineering for functional testing and complex knowledge base verification using matrices and structural testing strategies applied to knowledge-based systems.

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Progress in pattern recognition, Image Analysis, Computer Vision, and Applications 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Isabelle Bloch Roberto M. Cesar

ISBN: (数字)9783642166877

ISBN: (纸本)9783642166860

pattern recognition is a central topic in contemporary computer sciences, with continuously evolving topics, challenges, and methods, including machine learning, content-based image retrieval, and model- and knowledge-based - proaches, just to name a few. the Iberoamerican Congress on pattern Recog- tion (CIARP) has become established as a high-quality conference, highlighting the recent evolution of the domain. these proceedings include all papers presented during the 15th edition of this conference, held in Sao Paulo, Brazil, in November 2010. As was the case for previous conferences, CIARP 2010 attracted parti- pants from around the world with the aim of promoting and disseminating - going research on mathematical methods and computing techniques for pattern recognition, computer vision, image analysis, and speech recognition, as well as their applications in such diverse areas as robotics, health, entertainment, space exploration, telecommunications, data mining, document analysis,and natural language processing and recognition, to name only a few of them. Moreover, it provided a forum for scienti?c research, experience exchange, sharing new kno- edge and increasing cooperation between research groups in pattern recognition and related areas. It is important to underline that these conferences have contributed sign- icantly to the growth of national associations for pattern recognition in the Iberoamerican region, all of them as members of the international Association for pattern recognition (IAPR).

关键词： pattern recognition Image Processing and Computer Vision Artificial Intelligence Computer Imaging, Vision, pattern recognition and Graphics Biometrics Algorithm Analysis and Problem Complexity

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