Conferences related to Pattern Recognition

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ICASSP 2017 - 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


2014 IEEE Information Theory Workshop (ITW)

ITW2014 is a forum for technical exchange among scientists and engineers working on the fundamentals of information theory. The agenda is broad and will cover the diverse topics that information theory presently impacts. There will be both invited and contributed sessions.

  • 2012 IEEE Information Theory Workshop (ITW 2012)

    The past decade has seen an exponential increase in the data stored in distributed locations in various forms including corporate & personal data, multimedia, and medical data in repositories. The grand challenge is to store, process and transfer this massive amount of data, efficiently and securely over heterogeneous communication networks.

  • 2010 IEEE Information Theory Workshop (ITW 2010)

    Algebraic Methods in Communications Technology

  • 2009 IEEE Information Theory Workshop (ITW 2009)

    Covers the most relevant topics in Information Theory and Coding Theory of interest to the most recent applications to wireless networks, sensor networks, and biology

  • 2008 IEEE Information Theory Workshop (ITW 2008)

    This workshop will take a brief look into the recent information theory past to commemorate the 60th anniversary of Shannon's landmark paper, and then proceed to explore opportunities for information theory research in quantum computation, biology, statistics, and computer science.

  • 2006 IEEE Information Theory Workshop (ITW 2006)


2014 IEEE International Conference on Systems, Man and Cybernetics - SMC

SMC2014 targets advances in Systems Science and Engineering, Human-Machine Systems, and Cybernetics involving state-of-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies, and emerging applications.

  • 2013 IEEE International Conference on Systems, Man and Cybernetics - SMC

    SMC 2013 targets advances in Systems Science and Engineering Human-machine Systems and Cybernetics involving state-of-the-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies and emerging applications.

  • 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC

    Theory, research and technology advances including applications in all aspects of systems science and engineering, human machine systems, and emerging cybernetics.

  • 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC

    Theory, research, and technology advances including applications in all aspects of systems science and engineering, human machine systems, and emerging cybernetics.

  • 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC

    The 2010 IEEE International Conference on Systems, Man, and Cybernetics (SMC2010) provides an international forum that brings together those actively involved in areas of interest to the IEEE Systems, Man, and Cybernetics Society, to report on up-to-the-minute innovations and developments, to summarize the state-of-the-art, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics.

  • 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC

    The 2009 IEEE International Conference on Systems, Man, and Cybernetics (SMC2009) provides an international forum that brings together those actively involved in areas of interest to the IEEE Systems, Man, and Cybernetics Society, to report on up-to-the-minute innovations and developments, to summarize the state-of-the-art, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics.


2014 IEEE International Symposium on Information Theory (ISIT)

Annual international symposium on processing, transmission, storage, and use of information, as well as theoretical and applied aspects of coding, communications, and communications networks.


2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

AVSS focuses on video and signal based surveillance. Topics include: 1) Sensors and data fusion, 2) Processing, detection & recognition, 3) Analytics, behavior & biometrics, 4) Data management and human-computer interfaces, 5) Applications and 6) Privacy Issues


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Periodicals related to Pattern Recognition

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Advanced Packaging, IEEE Transactions on

The IEEE Transactions on Advanced Packaging has its focus on the modeling, design, and analysis of advanced electronic, photonic, sensors, and MEMS packaging.


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Components and Packaging Technologies, IEEE Transactions on

Component parts, hybrid microelectronics, materials, packaging techniques, and manufacturing technology.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Computational Intelligence Magazine, IEEE

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.


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Most published Xplore authors for Pattern Recognition

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Xplore Articles related to Pattern Recognition

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Discrete wavelet face graph matching

K. Ma; Xiaoou Tang Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2001

We have designed a new face graph model, using the discrete wavelet transform, for fast elastic bunch graph matching. With fiducial points as graph nodes, and local-area power spectrum vectors estimated from a space-frequency tree as node attributes, the DWT graph achieves similar model matching performance to the Gabor graph, but with orders of magnitude faster computation


Space propulsion systems diagnostics

S. Tulpule AUTOTESTCON '90. IEEE Systems Readiness Technology Conference. 'Advancing Mission Accomplishment', Conference Record., 1990

The author presents a systems-level approach to integrating state-of-the-art rocket engine technology with advanced computational techniques to develop an integrated diagnostic system (IDS) for space propulsion systems. The key feature of this IDS is the use of advanced diagnostic algorithms for failure detection as opposed to the practice of redline-based failure detection methods. The author presents a top-down analysis of ...


A radial basis function neural network with on-chip learning

Chin Park; K. Buckmann; J. Diamond; U. Santoni; Siang-Chun The; M. Holler; M. Glier; C. L. Scofield; L. Nunez Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 1993

A radial basis function neural network is implemented in a 0.8 μm Flash EPROM CMOS technology. The RBF network is used to estimate probability density functions for the purpose of pattern recognition. At 40 MHz this 3.7 M transistor chip performs 20 billion 5 b integer subtract and accumulate operations/s and 160 MFLOPS.


A new method of color image segmentation based on intensity and hue clustering

Chi Zhang; P. Wang Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000

A new method of color image segmentation is proposed. It is based on the K-means algorithm in HSI color space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The ...


Track-Clustering Error Evaluation for Track-Based Multi-camera Tracking System Employing Human Re-identification

Chih-Wei Wu; Meng-Ting Zhong; Yu Tsao; Shao-Wen Yang; Yen-Kuang Chen; Shao-Yi Chien 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017

In this study, we present a set of new evaluation measures for the track-based multi-camera tracking (T-MCT) task leveraging the clustering measurements. We demonstrate that the proposed evaluation measures provide notable advantages over previous ones. Moreover, a distributed and online T-MCT framework is proposed, where re-identification (Re-id) is embedded in T-MCT, to confirm the validity of the proposed evaluation measures. ...


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Educational Resources on Pattern Recognition

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eLearning

Discrete wavelet face graph matching

K. Ma; Xiaoou Tang Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2001

We have designed a new face graph model, using the discrete wavelet transform, for fast elastic bunch graph matching. With fiducial points as graph nodes, and local-area power spectrum vectors estimated from a space-frequency tree as node attributes, the DWT graph achieves similar model matching performance to the Gabor graph, but with orders of magnitude faster computation


Space propulsion systems diagnostics

S. Tulpule AUTOTESTCON '90. IEEE Systems Readiness Technology Conference. 'Advancing Mission Accomplishment', Conference Record., 1990

The author presents a systems-level approach to integrating state-of-the-art rocket engine technology with advanced computational techniques to develop an integrated diagnostic system (IDS) for space propulsion systems. The key feature of this IDS is the use of advanced diagnostic algorithms for failure detection as opposed to the practice of redline-based failure detection methods. The author presents a top-down analysis of ...


A radial basis function neural network with on-chip learning

Chin Park; K. Buckmann; J. Diamond; U. Santoni; Siang-Chun The; M. Holler; M. Glier; C. L. Scofield; L. Nunez Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 1993

A radial basis function neural network is implemented in a 0.8 μm Flash EPROM CMOS technology. The RBF network is used to estimate probability density functions for the purpose of pattern recognition. At 40 MHz this 3.7 M transistor chip performs 20 billion 5 b integer subtract and accumulate operations/s and 160 MFLOPS.


A new method of color image segmentation based on intensity and hue clustering

Chi Zhang; P. Wang Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000

A new method of color image segmentation is proposed. It is based on the K-means algorithm in HSI color space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The ...


Track-Clustering Error Evaluation for Track-Based Multi-camera Tracking System Employing Human Re-identification

Chih-Wei Wu; Meng-Ting Zhong; Yu Tsao; Shao-Wen Yang; Yen-Kuang Chen; Shao-Yi Chien 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017

In this study, we present a set of new evaluation measures for the track-based multi-camera tracking (T-MCT) task leveraging the clustering measurements. We demonstrate that the proposed evaluation measures provide notable advantages over previous ones. Moreover, a distributed and online T-MCT framework is proposed, where re-identification (Re-id) is embedded in T-MCT, to confirm the validity of the proposed evaluation measures. ...


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IEEE-USA E-Books

  • Frontmatter

    The prelims comprise: Half Title Wiley Series Page Title Copyright Dedication Contents Foreword Preface About the Authors

  • Pattern Discovery, Pattern Recognition, and System Identification

    March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

  • Smart Transformer Condition Monitoring and Diagnosis

    Transformer moisture estimation should be conducted based on a comprehensive approach, which combines online moisture???in???oil measurement, multi???physics modeling, and theoretical derivations. This chapter presents an intelligent framework for transformer condition monitoring and diagnosis. A number of signal and data analysis techniques, which have been implemented within the proposed framework, are also described. The techniques are: digital signal processing, which is concerned with de???noising and extracting signals acquired by online sensor measurements; pattern recognition, which focuses on identifying transformer fault types; data fusion, which integrates results from different sources to determine transformer condition; and determining the health index of a transformer insulation system. A variety of case studies are provided in the chapter to demonstrate the applications of these techniques. The chapter presents a hardware and software platform for implementing the proposed intelligent transformer condition monitoring and diagnosis framework and deploying the above signal and data analysis techniques.

  • Network Equations Used in the Simulations of Chapter 4

    This chapter contains sections titled: Differences between the model of Chapter 4 and Chapter 5, Cell equations, Simulation parameters

  • Introduction to Pattern Recognition and Data Mining

    This introductory chapter of the book provides a brief review of pattern recognition, data mining, and application of pattern recognition algorithms in data mining problems. The objective of the book is to provide some results of investigations, both theoretical and experimental, addressing the relevance of rough-fuzzy approaches to pattern recognition with real-life applications. The chapter first briefly presents a description of the basic concept, features, and techniques of pattern recognition. It then elaborates the data mining aspect, discussing its components, tasks involved, approaches, and application areas. The chapter next introduces the pattern recognition perspective of data mining and mentions related research challenges. It describes the role of soft computing in pattern recognition and data mining. Finally, the chapter discusses the scope and organization of the book. fuzzy logic

  • Self-Adaptation in Evolutionary Computation

    March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

  • Artificial Immune System

    Artificial immune system (AIS) is a computational intelligence system inspired by the working mechanism and principle of biological immune system (BIS). BIS makes use of innate immunity and adaptive immunity systems to generate accurate immune response against the invading antigens. The two systems mutually cooperate to resist the invasion of external antigens. The key to designing the AIS is to take full advantage of the immunology principles and to replicate the effectiveness and capability of the BIS in computer systems. Most of the AISs and malware detection methods have some deficiencies and shortcomings, which stimulates researchers to explore more efficient models and algorithms, including negative selection algorithm, clonal selection algorithm, immune network model, Danger theory, and immune concentration. At present, AIS has been widely used in many fields such as pattern recognition, function optimization, computer security, robot control, and data analysis.

  • Glossary

    Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural networks for pattern classification, Nigrin expands on these networks to present fundamentally new architectures that perform realtime pattern classification of embedded and synonymous patterns and that will aid in tasks such as vision, speech recognition, sensor fusion, and constraint satisfaction.Nigrin presents the new architectures in two stages. First he presents a network called Sonnet 1 that already achieves important properties such as the ability to learn and segment continuously varied input patterns in real time, to process patterns in a context sensitive fashion, and to learn new patterns without degrading existing categories. He then removes simplifications inherent in Sonnet 1 and introduces radically new architectures. These architectures have the power to classify patterns that may have similar meanings but that have different external appearances (synonyms). They also have been designed to represent patterns in a distributed fashion, both in short-term and long-term memory.Albert Nigrin is Assistant Professor in the Department of Computer Science and Information Systems at American University.

  • Rough-Fuzzy Hybridization and Granular Computing

    During the past decade, there have been several attempts to derive hybrid methods by judiciously combining the merits of fuzzy logic and rough sets under the name rough-fuzzy or fuzzy-rough computing. The result is a more intelligent and robust system providing a human interpretable, low cost, approximate solution, as compared to traditional techniques. This chapter discusses some of the theoretical developments relevant to pattern recognition. It first briefly introduces the necessary notions of fuzzy sets and rough sets. The chapter then discusses the concepts of granular computing and fuzzy granulation and emergence of rough-fuzzy computing. It presents a mathematical framework of generalized rough sets for uncertainty handling and defining rough entropy. Finally, the chapter discusses various roughness and entropy measures with properties. fuzzy logic; fuzzy set theory; rough set theory

  • Solar Image Processing and Analysis

    This chapter contains sections titled: Automatic Extraction of Filaments Solar Flare Detection Solar Corona Mass Ejection Detection References