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Link de la búsquedaMathematical Methodologies in Pattern Recognition and Machine Learning / SpringerLink (Online service) ; Pedro Latorre Carmona ; J. Salvador Sánchez ; Ana L. N. Fred (2013)
Título : Mathematical Methodologies in Pattern Recognition and Machine Learning : Contributions from the International Conference on Pattern Recognition Applications and Methods, 2012 Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Pedro Latorre Carmona ; J. Salvador Sánchez ; Ana L. N. Fred Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009 num. 30 Número de páginas: VIII, 196 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-5076-4 Idioma : Inglés (eng) Palabras clave: Mathematics Computer science Pattern recognition System theory Mathematical optimization Systems Theory, Control Optimization Math Applications in Science Recognition Clasificación: 51 Matemáticas Resumen: This volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2012,) held in Vilamoura, Algarve, Portugal from February 6th-8th, 2012. The conference provided a major point of collaboration between researchers, engineers and practitioners in the areas of Pattern Recognition, both from theoretical and applied perspectives, with a focus on mathematical methodologies. Contributions describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, and experimental and theoretical studies which yield new insights that provide key advances in the field. This book will be suitable for scientists and researchers in optimization, numerical methods, computer science, statistics and for differential geometers and mathematical physicists Nota de contenido: On order equivalences between distance and similarity measures on sequences and trees -- Scalable Corpus Annotation by Graph Construction and Label Propagation -- Computing the reeb graph for triangle meshes with active contours -- Efficient Computation of Voronoi Neighbors based on Polytope search in Pattern Recognition -- Estimation of the common oscillation for Phase Locked Matrix Factorization -- ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines -- Pitch-sensitive Components emerge from Hierarchical Sparse Coding of Natural Sounds -- Generative Embeddings based on Rican Mixtures: Application to KernelBased Discriminative Classification of Magnetic Resonance Images.-Single-Frame Signal Recovery Using a Similarity-Prior Based on Pearson Type VII MRF -- Tracking solutions of time varying linear inverse problems -- Stacked Conditional Random Fields Exploiting Structural Consistencies -- Segmentation of Vessel Geometries from Medical Images using GPF Deformable Model -- Robust Deformable Model for Segmenting the Left Ventricle in 3D volumes of Ultrasound Data -- Algorithm to maintain linear element in 3D Level Set Topology Optimization -- Facial Expression recognition using Log-Euclidean statistical shape models En línea: http://dx.doi.org/10.1007/978-1-4614-5076-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32239 Mathematical Methodologies in Pattern Recognition and Machine Learning : Contributions from the International Conference on Pattern Recognition Applications and Methods, 2012 [documento electrónico] / SpringerLink (Online service) ; Pedro Latorre Carmona ; J. Salvador Sánchez ; Ana L. N. Fred . - New York, NY : Springer New York : Imprint: Springer, 2013 . - VIII, 196 p : online resource. - (Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009; 30) .
ISBN : 978-1-4614-5076-4
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer science Pattern recognition System theory Mathematical optimization Systems Theory, Control Optimization Math Applications in Science Recognition Clasificación: 51 Matemáticas Resumen: This volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2012,) held in Vilamoura, Algarve, Portugal from February 6th-8th, 2012. The conference provided a major point of collaboration between researchers, engineers and practitioners in the areas of Pattern Recognition, both from theoretical and applied perspectives, with a focus on mathematical methodologies. Contributions describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, and experimental and theoretical studies which yield new insights that provide key advances in the field. This book will be suitable for scientists and researchers in optimization, numerical methods, computer science, statistics and for differential geometers and mathematical physicists Nota de contenido: On order equivalences between distance and similarity measures on sequences and trees -- Scalable Corpus Annotation by Graph Construction and Label Propagation -- Computing the reeb graph for triangle meshes with active contours -- Efficient Computation of Voronoi Neighbors based on Polytope search in Pattern Recognition -- Estimation of the common oscillation for Phase Locked Matrix Factorization -- ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines -- Pitch-sensitive Components emerge from Hierarchical Sparse Coding of Natural Sounds -- Generative Embeddings based on Rican Mixtures: Application to KernelBased Discriminative Classification of Magnetic Resonance Images.-Single-Frame Signal Recovery Using a Similarity-Prior Based on Pearson Type VII MRF -- Tracking solutions of time varying linear inverse problems -- Stacked Conditional Random Fields Exploiting Structural Consistencies -- Segmentation of Vessel Geometries from Medical Images using GPF Deformable Model -- Robust Deformable Model for Segmenting the Left Ventricle in 3D volumes of Ultrasound Data -- Algorithm to maintain linear element in 3D Level Set Topology Optimization -- Facial Expression recognition using Log-Euclidean statistical shape models En línea: http://dx.doi.org/10.1007/978-1-4614-5076-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32239 Ejemplares
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Título : Technical Analysis for Algorithmic Pattern Recognition Tipo de documento: documento electrónico Autores: Prodromos E. Tsinaslanidis ; Achilleas D. Zapranis ; SpringerLink (Online service) Editorial: Cham : Springer International Publishing Fecha de publicación: 2016 Otro editor: Imprint: Springer Número de páginas: XIII, 204 p. 55 illus., 26 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-319-23636-0 Idioma : Inglés (eng) Palabras clave: Finance Pattern recognition Economics, Mathematical Statistics Econometrics Macroeconomics Finance, general for Business/Economics/Mathematical Finance/Insurance Recognition Quantitative Macroeconomics/Monetary Economics//Financial Economics Clasificación: 330 Economía en general Resumen: The main purpose of this book is to resolve deficiencies and limitations that currently exist when using Technical Analysis (TA). Particularly, TA is being used either by academics as an "economic test" of the weak-form Efficient Market Hypothesis (EMH) or by practitioners as a main or supplementary tool for deriving trading signals. This book approaches TA in a systematic way utilizing all the available estimation theory and tests. This is achieved through the developing of novel rule-based pattern recognizers, and the implementation of statistical tests for assessing the importance of realized returns. More emphasis is given to technical patterns where subjectivity in their identification process is apparent. Our proposed methodology is based on the algorithmic and thus unbiased pattern recognition. The unified methodological framework presented in this book can serve as a benchmark for both future academic studies that test the null hypothesis of the weak-form EMH and for practitioners that want to embed TA within their trading/investment decision making processes. Nota de contenido: Technical Analysis -- Preprocessing Procedures -- Assessing the Predictive Performance of Technical Analysis -- Horizontal Patterns -- Zigzag Patterns -- Circular Patterns -- Technical Indicators -- A Statistical Assessment -- Dynamic Time Warping for Pattern Recognition En línea: http://dx.doi.org/10.1007/978-3-319-23636-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=42160 Technical Analysis for Algorithmic Pattern Recognition [documento electrónico] / Prodromos E. Tsinaslanidis ; Achilleas D. Zapranis ; SpringerLink (Online service) . - Cham : Springer International Publishing : Imprint: Springer, 2016 . - XIII, 204 p. 55 illus., 26 illus. in color : online resource.
ISBN : 978-3-319-23636-0
Idioma : Inglés (eng)
Palabras clave: Finance Pattern recognition Economics, Mathematical Statistics Econometrics Macroeconomics Finance, general for Business/Economics/Mathematical Finance/Insurance Recognition Quantitative Macroeconomics/Monetary Economics//Financial Economics Clasificación: 330 Economía en general Resumen: The main purpose of this book is to resolve deficiencies and limitations that currently exist when using Technical Analysis (TA). Particularly, TA is being used either by academics as an "economic test" of the weak-form Efficient Market Hypothesis (EMH) or by practitioners as a main or supplementary tool for deriving trading signals. This book approaches TA in a systematic way utilizing all the available estimation theory and tests. This is achieved through the developing of novel rule-based pattern recognizers, and the implementation of statistical tests for assessing the importance of realized returns. More emphasis is given to technical patterns where subjectivity in their identification process is apparent. Our proposed methodology is based on the algorithmic and thus unbiased pattern recognition. The unified methodological framework presented in this book can serve as a benchmark for both future academic studies that test the null hypothesis of the weak-form EMH and for practitioners that want to embed TA within their trading/investment decision making processes. Nota de contenido: Technical Analysis -- Preprocessing Procedures -- Assessing the Predictive Performance of Technical Analysis -- Horizontal Patterns -- Zigzag Patterns -- Circular Patterns -- Technical Indicators -- A Statistical Assessment -- Dynamic Time Warping for Pattern Recognition En línea: http://dx.doi.org/10.1007/978-3-319-23636-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=42160 Ejemplares
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Título : Computer Vision Using Local Binary Patterns Tipo de documento: documento electrónico Autores: Matti Pietikäinen ; SpringerLink (Online service) ; Abdenour Hadid ; Guoying Zhao ; Timo Ahonen Editorial: London : Springer London Fecha de publicación: 2011 Otro editor: Imprint: Springer Colección: Computational Imaging and Vision, ISSN 1381-6446 num. 40 Número de páginas: XVI, 212 p Il.: online resource ISBN/ISSN/DL: 978-0-85729-748-8 Idioma : Inglés (eng) Palabras clave: Mathematics Computer graphics Image processing Pattern recognition Biometrics (Biology) Mathematics, general Imaging, Vision, Recognition and Graphics Processing Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: - Local binary patterns and their variants in spatial and spatiotemporal domains - Texture classification and segmentation, description of interest regions - Applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures - Background subtraction, recognition of actions - Face analysis using still images and image sequences, visual speech recognition - LBP in various applications Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision Nota de contenido: Background -- Local binary patterns for still images -- Spatiotemporal LBP -- Texture classification and segmentation -- Description of interest regions -- Applications in image retrieval and 3D recognition -- Recognition and segmentation of dynamic textures -- Background subtraction -- Recognition of actions -- Face analysis using still images -- Face analysis using image sequences -- Visual recognition of spoken phrases -- LBP in different applications En línea: http://dx.doi.org/10.1007/978-0-85729-748-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33135 Computer Vision Using Local Binary Patterns [documento electrónico] / Matti Pietikäinen ; SpringerLink (Online service) ; Abdenour Hadid ; Guoying Zhao ; Timo Ahonen . - London : Springer London : Imprint: Springer, 2011 . - XVI, 212 p : online resource. - (Computational Imaging and Vision, ISSN 1381-6446; 40) .
ISBN : 978-0-85729-748-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer graphics Image processing Pattern recognition Biometrics (Biology) Mathematics, general Imaging, Vision, Recognition and Graphics Processing Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: - Local binary patterns and their variants in spatial and spatiotemporal domains - Texture classification and segmentation, description of interest regions - Applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures - Background subtraction, recognition of actions - Face analysis using still images and image sequences, visual speech recognition - LBP in various applications Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision Nota de contenido: Background -- Local binary patterns for still images -- Spatiotemporal LBP -- Texture classification and segmentation -- Description of interest regions -- Applications in image retrieval and 3D recognition -- Recognition and segmentation of dynamic textures -- Background subtraction -- Recognition of actions -- Face analysis using still images -- Face analysis using image sequences -- Visual recognition of spoken phrases -- LBP in different applications En línea: http://dx.doi.org/10.1007/978-0-85729-748-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33135 Ejemplares
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Título : Statistical Methods in Counterterrorism : Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Alyson G. Wilson ; Wilson, Gregory D ; David H. Olwell Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Número de páginas: XII, 292 p. 14 illus Il.: online resource ISBN/ISSN/DL: 978-0-387-35209-1 Idioma : Inglés (eng) Palabras clave: Statistics Pattern recognition Game theory Operations research Management science Economic Statistical Theory and Methods Theory, Economics, Social Behav. Sciences Theory/Quantitative Economics/Mathematical Signal, Image Speech Processing Recognition Research, Science Clasificación: 51 Matemáticas Resumen: All the data was out there to warn us of this impending attack, why didn't we see it?" This was a frequently asked question in the weeks and months after the terrorist attacks on the World Trade Center and the Pentagon on September 11, 2001. In the wake of the attacks, statisticians moved quickly to become part of the national response to the global war on terror. This book is an overview of the emerging research program at the intersection of national security and statistical sciences. A wide range of talented researchers address issues in . Syndromic Surveillance---How do we detect and recognize bioterrorist events? . Modeling and Simulation---How do we better understand and explain complex processes so that decision makers can take the best course of action? . Biometric Authentication---How do we pick the terrorist out of the crowd of faces or better match the passport to the traveler? . Game Theory---How do we understand the rules that terrorists are playing by? This book includes technical treatments of statistical issues that will be of use to quantitative researchers as well as more general examinations of quantitative approaches to counterterrorism that will be accessible to decision makers with stronger policy backgrounds. Dr. Alyson G. Wilson is a statistician and the technical lead for DoD programs in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. Gregory D. Wilson is a rhetorician and ethnographer in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. David H. Olwell is chair of the Department of Systems Engineering at the Naval Postgraduate School in Monterey, California Nota de contenido: Game Theory -- Game Theory in an Age of Terrorism: How Can Statisticians Contribute? -- Combining Game Theory and Risk Analysis in Counterterrorism: A Smallpox Example -- Game-Theoretic and Reliability Methods in Counterterrorism and Security -- Biometric Authentication -- Biometric Authentication -- Towards Statistically Rigorous Biometric Authentication Using Facial Images -- Recognition Problem of Biometrics: Nonparametric Dependence Measures and Aggregated Algorithms -- Syndromic Surveillance -- Data Analysis Research Issues and Emerging Public Health Biosurveillance Directions -- Current and Potential Statistical Methods for Monitoring Multiple Data Streams for Biosurveillance -- Evaluating Statistical Methods for Syndromic Surveillance -- A Spatiotemporal Analysis of Syndromic Data for Biosurveillance -- Modeling -- Modeling and Simulation for Defense and National Security -- Modeling and Parameterization for a Smallpox Simulation Study -- Approaches to Modeling the Concentration Field for Adaptive Sampling of Contaminants during Site Decontamination -- Secure Statistical Analysis of Distributed Databases -- Statistical Evaluation of the Impact of Background Suppression on the Sensitivity of Passive Radiation Detectors En línea: http://dx.doi.org/10.1007/0-387-35209-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34816 Statistical Methods in Counterterrorism : Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication [documento electrónico] / SpringerLink (Online service) ; Alyson G. Wilson ; Wilson, Gregory D ; David H. Olwell . - New York, NY : Springer New York, 2006 . - XII, 292 p. 14 illus : online resource.
ISBN : 978-0-387-35209-1
Idioma : Inglés (eng)
Palabras clave: Statistics Pattern recognition Game theory Operations research Management science Economic Statistical Theory and Methods Theory, Economics, Social Behav. Sciences Theory/Quantitative Economics/Mathematical Signal, Image Speech Processing Recognition Research, Science Clasificación: 51 Matemáticas Resumen: All the data was out there to warn us of this impending attack, why didn't we see it?" This was a frequently asked question in the weeks and months after the terrorist attacks on the World Trade Center and the Pentagon on September 11, 2001. In the wake of the attacks, statisticians moved quickly to become part of the national response to the global war on terror. This book is an overview of the emerging research program at the intersection of national security and statistical sciences. A wide range of talented researchers address issues in . Syndromic Surveillance---How do we detect and recognize bioterrorist events? . Modeling and Simulation---How do we better understand and explain complex processes so that decision makers can take the best course of action? . Biometric Authentication---How do we pick the terrorist out of the crowd of faces or better match the passport to the traveler? . Game Theory---How do we understand the rules that terrorists are playing by? This book includes technical treatments of statistical issues that will be of use to quantitative researchers as well as more general examinations of quantitative approaches to counterterrorism that will be accessible to decision makers with stronger policy backgrounds. Dr. Alyson G. Wilson is a statistician and the technical lead for DoD programs in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. Gregory D. Wilson is a rhetorician and ethnographer in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. David H. Olwell is chair of the Department of Systems Engineering at the Naval Postgraduate School in Monterey, California Nota de contenido: Game Theory -- Game Theory in an Age of Terrorism: How Can Statisticians Contribute? -- Combining Game Theory and Risk Analysis in Counterterrorism: A Smallpox Example -- Game-Theoretic and Reliability Methods in Counterterrorism and Security -- Biometric Authentication -- Biometric Authentication -- Towards Statistically Rigorous Biometric Authentication Using Facial Images -- Recognition Problem of Biometrics: Nonparametric Dependence Measures and Aggregated Algorithms -- Syndromic Surveillance -- Data Analysis Research Issues and Emerging Public Health Biosurveillance Directions -- Current and Potential Statistical Methods for Monitoring Multiple Data Streams for Biosurveillance -- Evaluating Statistical Methods for Syndromic Surveillance -- A Spatiotemporal Analysis of Syndromic Data for Biosurveillance -- Modeling -- Modeling and Simulation for Defense and National Security -- Modeling and Parameterization for a Smallpox Simulation Study -- Approaches to Modeling the Concentration Field for Adaptive Sampling of Contaminants during Site Decontamination -- Secure Statistical Analysis of Distributed Databases -- Statistical Evaluation of the Impact of Background Suppression on the Sensitivity of Passive Radiation Detectors En línea: http://dx.doi.org/10.1007/0-387-35209-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34816 Ejemplares
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Título : Statistics and Analysis of Shapes Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Hamid Krim ; Anthony Yezzi Editorial: Boston, MA : Birkhäuser Boston Fecha de publicación: 2006 Colección: Modeling and Simulation in Science, Engineering and Technology, ISSN 2164-3679 Número de páginas: XII, 396 p. 143 illus Il.: online resource ISBN/ISSN/DL: 978-0-8176-4481-9 Idioma : Inglés (eng) Palabras clave: Mathematics Pattern recognition Visualization Mathematical models Probabilities Statistics Modeling and Industrial Probability Theory Stochastic Processes for Life Sciences, Medicine, Health Sciences Recognition Signal, Image Speech Processing Clasificación: 51 Matemáticas Resumen: The subject of pattern analysis and recognition pervades many aspects of our daily lives, including user authentication in banking, object retrieval from databases in the consumer sector, and the omnipresent surveillance and security measures around sensitive areas. Shape analysis, a fundamental building block in many approaches to these applications, is also used in statistics, biomedical applications (Magnetic Resonance Imaging), and many other related disciplines. With contributions from some of the leading experts and pioneers in the field, this self-contained, unified volume is the first comprehensive treatment of theory, methods, and algorithms, available in a single resource, without the typical quagmire of vast information scattered over a wide body of literature. Developments are discussed from a rapidly increasing number of research papers in diverse fields, including the mathematical and physical sciences, engineering, and medicine. The initial chapters explore the statistical modeling of landmarks while subsequent chapters address the probabilistic modeling of entire shapes. The latter part of the book, with the exception of the last two chapters, concentrates on case studies as well as implementational and practical challenges in real systems. Extensive illustrations throughout help readers overcome the sometimes terse technical details of the geometric and probabilistic formalism. Knowledge of advanced calculus and basic statistics and probability theory are the only prerequisites for the reader. Statistics and Analysis of Shapes will be an essential learning kit for statistical researchers, engineers, scientists, medical researchers, and students seeking a rapid introduction to the field. It may be used as a textbook for a graduate-level special topics course in statistics and signal/image analysis, or for an intensive short course on shape analysis and modeling. The state-of-the-art techniques presented will also be useful for experienced researchers and practitioners in academia and industry Nota de contenido: Medial Axis Computation and Evolution -- Shape Variation of Medial Axis Representations via Principal Geodesic Analysis on Symmetric Spaces -- 2D Shape Modeling using Skeletal Graphs in a Morse Theoretic Framework -- Matching with Shape Contexts -- Shape Recognition Based on an a Contrario Methodology -- Integral Invariants and Shape Matching -- On the Representation of Shapes Using Implicit Functions -- Computing with Point Cloud Data -- Determining Intrinsic Dimension and Entropy of High-Dimensional Shape Spaces -- Object-Image Metrics for Generalized Weak Perspective Projection -- Wulff Shapes at Zero Temperature for Some Models Used in Image Processing -- Curve Shortening and Interacting Particle Systems -- Riemannian Structures on Shape Spaces: A Framework for Statistical Inferences -- Modeling Planar Shape Variation via Hamiltonian Flows of Curves -- Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics En línea: http://dx.doi.org/10.1007/0-8176-4481-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34870 Statistics and Analysis of Shapes [documento electrónico] / SpringerLink (Online service) ; Hamid Krim ; Anthony Yezzi . - Boston, MA : Birkhäuser Boston, 2006 . - XII, 396 p. 143 illus : online resource. - (Modeling and Simulation in Science, Engineering and Technology, ISSN 2164-3679) .
ISBN : 978-0-8176-4481-9
Idioma : Inglés (eng)
Palabras clave: Mathematics Pattern recognition Visualization Mathematical models Probabilities Statistics Modeling and Industrial Probability Theory Stochastic Processes for Life Sciences, Medicine, Health Sciences Recognition Signal, Image Speech Processing Clasificación: 51 Matemáticas Resumen: The subject of pattern analysis and recognition pervades many aspects of our daily lives, including user authentication in banking, object retrieval from databases in the consumer sector, and the omnipresent surveillance and security measures around sensitive areas. Shape analysis, a fundamental building block in many approaches to these applications, is also used in statistics, biomedical applications (Magnetic Resonance Imaging), and many other related disciplines. With contributions from some of the leading experts and pioneers in the field, this self-contained, unified volume is the first comprehensive treatment of theory, methods, and algorithms, available in a single resource, without the typical quagmire of vast information scattered over a wide body of literature. Developments are discussed from a rapidly increasing number of research papers in diverse fields, including the mathematical and physical sciences, engineering, and medicine. The initial chapters explore the statistical modeling of landmarks while subsequent chapters address the probabilistic modeling of entire shapes. The latter part of the book, with the exception of the last two chapters, concentrates on case studies as well as implementational and practical challenges in real systems. Extensive illustrations throughout help readers overcome the sometimes terse technical details of the geometric and probabilistic formalism. Knowledge of advanced calculus and basic statistics and probability theory are the only prerequisites for the reader. Statistics and Analysis of Shapes will be an essential learning kit for statistical researchers, engineers, scientists, medical researchers, and students seeking a rapid introduction to the field. It may be used as a textbook for a graduate-level special topics course in statistics and signal/image analysis, or for an intensive short course on shape analysis and modeling. The state-of-the-art techniques presented will also be useful for experienced researchers and practitioners in academia and industry Nota de contenido: Medial Axis Computation and Evolution -- Shape Variation of Medial Axis Representations via Principal Geodesic Analysis on Symmetric Spaces -- 2D Shape Modeling using Skeletal Graphs in a Morse Theoretic Framework -- Matching with Shape Contexts -- Shape Recognition Based on an a Contrario Methodology -- Integral Invariants and Shape Matching -- On the Representation of Shapes Using Implicit Functions -- Computing with Point Cloud Data -- Determining Intrinsic Dimension and Entropy of High-Dimensional Shape Spaces -- Object-Image Metrics for Generalized Weak Perspective Projection -- Wulff Shapes at Zero Temperature for Some Models Used in Image Processing -- Curve Shortening and Interacting Particle Systems -- Riemannian Structures on Shape Spaces: A Framework for Statistical Inferences -- Modeling Planar Shape Variation via Hamiltonian Flows of Curves -- Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics En línea: http://dx.doi.org/10.1007/0-8176-4481-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34870 Ejemplares
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