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.

  • ICASSP 2015 - 2015 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.

  • ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

    ICASSP 2014 will be the world s largest and most comprehensive technical conference focused on the many facets of signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and oral/poster sessions on the most up-to-date topics in signal processing research.

  • ICASSP 2013 - 2013 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.

  • ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing

    The latest research results on both theories and applications on signal processing will be presented and discussed among participants from all over the world. Video/Speech Signal processing used in human interface between Robots and Personal users will be highlighted.

  • ICASSP 2011 - 2011 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 on: Audio and electroacoustics Bio imaging and signal processing Design and implementation of signal processing systems Image and multidimensional signal processing Industry technology tracks Information forensics and security.

  • ICASSP 2010 - 2010 IEEE International Conference on Acoustics, Speech and Signal Processing

    TBA

  • ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing

    The 34th ICASSP will be held in Taiwan April 19-24, 2009. 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.

  • ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing


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.


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.


Electronics Packaging Manufacturing, IEEE Transactions on

Design for manufacturability, cost and process modeling, process control and automation, factory analysis and improvement, information systems, statistical methods, environmentally-friendly processing, and computer-integrated manufacturing for the production of electronic assemblies, products, and systems.


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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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Invariant Pattern Recognition Using Radial Tchebichef Moments

Bin Xiao; Jian-Feng Ma; Jiang-Tao Cui 2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Radial Tchebichef moments as a discrete orthogonal moment in the polar coordinate have been successfully used in the field of pattern recognition. However, the scaling invariant property of these moments has not been studied due to the complexity of the problem. In this paper, we present a new method to construct a complete set of scaling and rotation invariants extract ...


Subspace-Based Multi-Channel Speech Enhancement Using a Novel Signal Subspace Dimension Estimator in Reverberant Environments

Chao Li; Wen-Ju Liu 2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Although the SSA has been studied extensively for speech enhancement, not too much attention has been paid to discuss the method to identify signal subspace dimension. In this paper we present a novel signal subspace dimension estimator based on Frobenius norm, with which subspace-based multi-channel speech enhancement is robust to such adverse acoustic environments as room reverberation and low input ...


CCPR 2010 [Title Page]

2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Presents the title page of the proceedings.


Class-Based Matching of Object Parts

E. Bart; S. Ullman 2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004

We develop a novel technique for class-based matching of object parts across large changes in viewing conditions. Given a set of images of objects from a given class under different viewing conditions, the algorithm identifies corresponding regions depicting the same object part in different images. The technique is based on using the equivalence of corresponding features in different viewing conditions. ...


A Reflection and Occlusion Robust Eye Center Searching Algorithm

Guoqing Xu; Yangsheng Wang; Mingcai Zhou; Xiangsheng Huang 2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Eye center detection is an essential module in iris segmentation and gaze tracking. It is more challengeable to achieve this goal using a usual web camera under natural illumination. The image resolution and the eye region scale are main problems. Focusing on solving these problems, this paper proposes a robust eye center searching algorithm which can locate the iris including ...


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Educational Resources on Pattern Recognition

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eLearning

Invariant Pattern Recognition Using Radial Tchebichef Moments

Bin Xiao; Jian-Feng Ma; Jiang-Tao Cui 2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Radial Tchebichef moments as a discrete orthogonal moment in the polar coordinate have been successfully used in the field of pattern recognition. However, the scaling invariant property of these moments has not been studied due to the complexity of the problem. In this paper, we present a new method to construct a complete set of scaling and rotation invariants extract ...


Subspace-Based Multi-Channel Speech Enhancement Using a Novel Signal Subspace Dimension Estimator in Reverberant Environments

Chao Li; Wen-Ju Liu 2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Although the SSA has been studied extensively for speech enhancement, not too much attention has been paid to discuss the method to identify signal subspace dimension. In this paper we present a novel signal subspace dimension estimator based on Frobenius norm, with which subspace-based multi-channel speech enhancement is robust to such adverse acoustic environments as room reverberation and low input ...


CCPR 2010 [Title Page]

2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Presents the title page of the proceedings.


Class-Based Matching of Object Parts

E. Bart; S. Ullman 2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004

We develop a novel technique for class-based matching of object parts across large changes in viewing conditions. Given a set of images of objects from a given class under different viewing conditions, the algorithm identifies corresponding regions depicting the same object part in different images. The technique is based on using the equivalence of corresponding features in different viewing conditions. ...


A Reflection and Occlusion Robust Eye Center Searching Algorithm

Guoqing Xu; Yangsheng Wang; Mingcai Zhou; Xiangsheng Huang 2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Eye center detection is an essential module in iris segmentation and gaze tracking. It is more challengeable to achieve this goal using a usual web camera under natural illumination. The image resolution and the eye region scale are main problems. Focusing on solving these problems, this paper proposes a robust eye center searching algorithm which can locate the iris including ...


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IEEE-USA E-Books

  • Special Session On Morphogenic 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

  • How to FTP Our Software

    Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning, particularly in the evolution of inductive reasoning from pattern recognition, through initial approaches to symbolic machine learning, to recent techniques for learning relational concepts. In this book they provide an extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software engineering applications, including inductive program development, testing, and maintenance.Inductive Logic Programming includes a definition of the basic ILP problem and its variations (incremental, with queries, for multiple predicates and predicate invention capabilities), a description of bottom-up operators and techniques (such as least general generalization, inverse resolution, and inverse implication), an analysis of top-down methods (mainly MIS and FOIL-like systems), and a survey of methods and languages for specifying inductive bias.Logic Programming series

  • List of Symbols

    Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC- Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

  • Further Readings

    Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning -- the foundation of efforts to process that data into knowledge -- has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number- crunching mainframes to mobile devices, putting today's machin learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.

  • Bibliography

    Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning, particularly in the evolution of inductive reasoning from pattern recognition, through initial approaches to symbolic machine learning, to recent techniques for learning relational concepts. In this book they provide an extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software engineering applications, including inductive program development, testing, and maintenance.Inductive Logic Programming includes a definition of the basic ILP problem and its variations (incremental, with queries, for multiple predicates and predicate invention capabilities), a description of bottom-up operators and techniques (such as least general generalization, inverse resolution, and inverse implication), an analysis of top-down methods (mainly MIS and FOIL-like systems), and a survey of methods and languages for specifying inductive bias.Logic Programming series

  • Author Index

    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

  • Physical modeling

    Recently, cellular automata machines with the size, speed, and flexibility for general experimentation at a moderate cost have become available to the scientific community. These machines provide a laboratory in which the ideas presented in this book can be tested and applied to the synthesis of a great variety of systems. Computer scientists and researchers interested in modeling and simulation as well as other scientists who do mathematical modeling will find this introduction to cellular automata and cellular automata machines (CAM) both useful and timely.Cellular automata are the computer scientist's counterpart to the physicist's concept of 'field' They provide natural models for many investigations in physics, combinatorial mathematics, and computer science that deal with systems extended in space and evolving in time according to local laws. A cellular automata machine is a computer optimized for the simulation of cellular automata. Its dedicated architecture allows it to run thousands of times faster than a general-purpose computer of comparable cost programmed to do the same task. In practical terms this permits intensive interactive experimentation and opens up new fields of research in distributed dynamics, including practical applications involving parallel computation and image processing.Contents: Introduction. Cellular Automata. The CAM Environment. A Live Demo. The Rules of the Game. Our First rules. Second-order Dynamics. The Laboratory. Neighbors and Neighborhood. Running. Particle Motion. The Margolus Neighborhood. Noisy Neighbors. Display and Analysis. Physical Modeling. Reversibility. Computing Machinery. Hydrodynamics. Statistical Mechanics. Other Applications. Imaging Processing. Rotations. Pattern Recognition. Multiple CAMS. Persp ectives and Conclusions.Tommaso Toffoli and Norman Margolus are researchers at the Laboratory for Computer Science at MIT. Cellular Automata Machines is included in the Scientific Computation Series, edited by Dennis Cannon.

  • References

    Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC- Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

  • Learning Pattern ClassificationA Survey

    Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik-Chervonenkis theory, and neural networks. The presentation and the large (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists.

  • Index

    Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning -- the foundation of efforts to process that data into knowledge -- has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number- crunching mainframes to mobile devices, putting today's machin learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.




Jobs related to Pattern Recognition

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