Continuous Primal-Dual methods for Image Processing
In this article we study a continuous Primal-Dual method proposed by Appleton and Talbot and generalize it to other problems in image processing. We interpret it as an Arrow-Hurwicz method which leads to a better description of the system of PDEs obtained. We show existence and uniqueness of solutions and get a convergence result for the denoising problem. Our analysis also yields new a posteriori estimates.
The Mathematics of Medical Imaging
A Beginner's Guide to the Mathematics of Medical Imaging presents the basic mathematics of computerized tomography – the CT scan – for an audience of undergraduates in mathematics and engineering. Assuming no prior background in advanced mathematical analysis, topics such as the Fourier transform, sampling, and discrete approximation algorithms are introduced from scratch and are developed within the context of medical imaging. A chapter on magnetic resonance imaging focuses on manipulation of the Bloch equation, the system of differential equations that is the foundation of this important technology.
The text is self-contained with a range of practical exercises, topics for further study, and an ample bibliography, making it ideal for use in an undergraduate course in applied or engineering mathematics, or by practitioners in radiology who want to know more about the mathematical foundations of their field.
G:\BT\CRC.Press.-.Algorithms.and.Theory.of.Computation.Handbook.Volume.II.Second.Edition.2009.Retail.Ebook-ATTiCA
Algorithms and Theory of Computation Handbook, Second Edition: Special Topics and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems.
Along with updating and revising many of the existing chapters, this second edition contains more than 15 new chapters. This edition now covers self-stabilizing and pricing algorithms as well as the theories of privacy and anonymity, databases, computational games, and communication networks. It also discusses computational topology, natural language processing, and grid computing and explores applications in intensity-modulated radiation therapy, voting, DNA research, systems biology, and financial derivatives.
This best-selling handbook continues to help computer professionals and engineers find significant information on various algorithmic topics. The expert contributors clearly define the terminology, present basic results and techniques, and offer a number of current references to the in-depth literature. They also provide a glimpse of the major research issues concerning the relevant topics.
CRC.Press.-.Algorithms.and.Theory.of.Computation.Handbook.Volume.I.Second.Edition.2009.Retail.Ebook-ATTiCA
Algorithms and Theory of Computation Handbook, Second Edition: General Concepts and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. Along with updating and revising many of the existing chapters, this second edition contains four new chapters that cover external memory and parameterized algorithms as well as computational number theory and algorithmic coding theory.
This best-selling handbook continues to help computer professionals and engineers find significant information on various algorithmic topics. The expert contributors clearly define the terminology, present basic results and techniques, and offer a number of current references to the in-depth literature. They also provide a glimpse of the major research issues concerning the relevant topics.
Springer.Markov.Random.Field.Modeling.In.Image.Analysis.3rd.Edition.Mar.2009.eBook-ELOHiM
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This second edition includes the most important progress in Markov modeling in image analysis in recent years such as Markov modeling of images with "macro" patterns (e.g. the FRAME model), Markov chain Monte Carlo (MCMC) methods, reversible jump MCMC. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Principles and Theory for Data Mining and Machine Learning
This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons.
Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature. There are numerous computed examples, complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning.
Bertrand Clarke is a Professor of Statistics in the Department of Medicine, Department of Epidemiology and Public Health, and the Center for Computational Sciences at the University of Miami. He has been on the Editorial Board of the Journal of the American Statistical Association, the Journal of Statistical Planning and Inference, and Statistical Papers. He is co-winner, with Andrew Barron, of the 1990 Browder J. Thompson Prize from the Institute of Electrical and Electronic Engineers.
Ernest Fokoue is an Assistant Professor of Statistics at Kettering University. He has also taught at Ohio State University and been a long term visitor at the Statistical and Mathematical Sciences Institute where he was a Post-doctoral Research Fellow in the Data Mining and Machine Learning Program. In 2000, he was the winner of the Young Researcher Award from the International Association for Statistical Computing.
Hao Helen Zhang is an Associate Professor of Statistics in the Department of Statistics at North Carolina State University. For 2003-2004, she was a Research Fellow at SAMSI and in 2007, she won a Faculty Early Career Development Award from the National Science Foundation. She is on the Editorial Board of the Journal of the American Statistical Association and Biometrics.
Semantics-Oriented Natural Language Processing: Mathematical Models and Algorithms
Substantially formal treatment of issues for designers of natural language processing systems
Presents an in-depth treatment of NL semantics and a mathematical model of a linguistics database
Extensive use of examples and illustrations to clarify complex material and demonstrate practical applications
End-of-chapter exercises, historical and bibliographical notes, and glossaries enrich the text
This book examines key issues in designing semantics-oriented natural language (NL) processing systems. One of the key features is an original strategy for transforming the existing World Wide Web into a new generation Semantic Web (SW-2) and the basic formal tools for its realization, which are proposed. The principal distinguishing feature of the proposed SW-2 is the well-developed ability of NL processing.
A broad conceptual framework for describing structured meanings of NL-texts (sentences and arbitrarily complex discourses) is obtained by introducing a mathematical model describing 10 interrelated partial operations on conceptual structures. A new class of formal languages called standard knowledge languages (SK-languages) is defined. Readers will gain knowledge of these languages and learn a way of building semantic representations using them.
Additionally, a broadly applicable mathematical model of a linguistic database is constructed. A useful for practice and strongly structured multi-lingual algorithm of semantic-syntactic analysis of NL-texts is described by means of original formal concepts; the input texts can be sentences in English, Russian, and German.
With extensive use of examples and illustrations to clarify complex material and demonstrate practical applications, many historical and bibliographical notes, end-of-chapter exercises, and glossaries, this book can serve as a graduate-level textbook, as well as a good reference for researchers and practitioners who deal with the various problems involving semantics of natural language texts, ontologies, Semantic Web, semantic data integration in e-science, and content languages in multi-agent systems, in particular, in e-commerce and e-health.
Continuous-Time Markov Decision Processes
Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer science, communications engineering, control of populations (such as fisheries and epidemics), and management science, among many other fields. This volume provides a unified, systematic, self-contained presentation of recent developments on the theory and applications of continuous-time MDPs. The MDPs in this volume include most of the cases that arise in applications, because they allow unbounded transition and reward/cost rates. Much of the material appears for the first time in book form.
Innovations in Machine Learning: Theory and Application (Studies in Fuzziness and Soft Computing, Volume 194)
Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neura
Sams - Windows Server 2008 R2 Unleashed (2010) (ATTiCA)
Windows Server 2008 R2 Unleashed is the most comprehensive and realistic guide to planning, design, prototyping, implementation, migration, administration, and support. Based on the authors’ unsurpassed experience working with Windows Server 2008 R2 since its earliest alpha releases, it offers indispensable guidance drawn from hundreds of production environments.
Microsoft MVP Rand Morimoto and his colleagues systematically introduce Windows Server 2008 R2 to IT professionals, identifying R2’s most crucial enhancements and walking through every step of installation and configuration. Next, they present comprehensive coverage of every area of Windows Server 2008 R2, including Active Directory, networking services, security, R2 migration from Windows Server 2003 and 2008, administration, fault tolerance, optimization and troubleshooting, core application services, and more.
The authors thoroughly address major Windows Server 2008 R2 enhancements and present extensive coverage of R2 innovations ranging from Hyper-V virtualization to DirectAccess and the enhancements in Failover Clustering. Every chapter contains tips, tricks, and best practices learned from actual deployments: practical information for using Windows Server 2008 R2 to solve real business problems.
Detailed information on how to...
Plan and migrate from Windows Server 2003/2008 to Windows Server 2008 R2 and use R2’s new server migration tools
Manage Active Directory with Active Directory Administrative Center, Best Practice Analyzer, and PowerShell scripts
Use R2’s updated security tools and templates to lock down servers, clients, and networks
Maximize availability with Windows Server 2008 R2 clustering, fault tolerance, and replication
Streamline client management with new Group Policy ADMX settings and management tools
Improve remote access using DirectAccess, Remote Desktop Services (formerly Terminal Services), and Virtual Desktop Infrastructure
Implement Hyper-V virtualization including the built-in Live Migration technology
Leverage add-ons such as Windows SharePoint Services, Windows Media Services, and IIS 7.5
Ant Colony Optimization And Swarm Intelligence -2006gpg.pdf
This book constitutes the refereed proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006.
The 27 revised full papers, 23 revised short papers, and 12 extended abstracts presented were carefully reviewed and selected from 115 submissions. The papers are devoted to theoretical and foundational aspects of ant algorithms, evolutionary optimization, ant colony optimization, and swarm intelligence and deal with a broad variety of optimization applications in networking, operations research, multiagent systems, robot systems, networking, etc.
Microsoft.Press.Windows.Internals.5th.Edition.Jun.2009
Get the architectural perspectives and inside details you need to understand how Windows operates.
See how the core components of the Windows operating system work behind the scenes—guided by a team of internationally renowned internals experts. Fully updated for Windows Server 2008 and Windows Vista, this classic guide delivers key architectural insights on system design, debugging, performance, and support—along with hands-on experiments to experience Windows internal behavior firsthand. Delve inside Windows architecture and internals:
Understand how the core system and management mechanisms work—from the object manager to services to the registry
Explore internal system data structures using tools like the kernel debugger
Grasp the scheduler's priority and CPU placement algorithms
Go inside the Windows security model to see how it authorizes access to data
Understand how Windows manages physical and virtual memory
Tour the Windows networking stack from top to bottom—including APIs, protocol drivers, and network adapter drivers
Troubleshoot file-system access problems and system boot problems
Learn how to analyze crashes
Genetic.Programming.On.the.Programming.of.Computers.by.Means.of.Natural.Selection.-.John.R.Koza
Genetic programming may be more powerful than neural networks and other machine learning techniques, able to solve problems in a wider range of disciplines. In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic Programming contains a great many worked examples and includes a sample computer code that will allow readers to run their own programs.
In getting computers to solve problems without being explicitly programmed, Koza stresses two points: that seemingly different problems from a variety of fields can be reformulated as problems of program induction, and that the recently developed genetic programming paradigm provides a way to search the space of possible computer programs for a highly fit individual computer program to solve the problems of program induction. Good programs are found by evolving them in a computer against a fitness measure instead of by sitting down and writing them.
John R. Koza is Consulting Associate Professor in the Computer Science Department at Stanford University.
Geometric Description of Images as Topographic Maps (Lecture Notes in Mathematics) By Vicent Caselles, Pascal Monasse
This volume discusses the basic geometric contents of an image and presents a tree data structure to handle those contents efficiently. The nodes of the tree are derived from connected components of level sets of the intensity, while the edges represent inclusion information. Grain filters, morphological operators simplifying these geometric contents, are analyzed and several applications to image comparison and registration, and to edge and corner detection, are presented.
The mathematically inclined reader may be most interested in Chapters 2 to 6, which generalize the topological Morse description to continuous or semicontinuous functions, while mathematical morphologists may more closely consider grain filters in Chapter 3. Computer scientists will find algorithmic considerations in Chapters 6 and 7, the full justification of which may be found in Chapters 2 and 4 respectively. Lastly, all readers can learn more about the motivation for this work in the image processing applications presented in Chapter 8.
Your Research Project:A Step-by-Step Guide for the First-Time Researcher
In this new edition of Your Research Project, Nicholas S.R. Walliman has made this bestselling book even better with the addition of a number of new features whilst retaining all the benefits of the original. New features include: more elaboration on the differing needs of masters and PhD students; a new overview of the entire research chronology from start to finish; student checklists throughout; a new chapter on research ethics; new sections on critical reading skills and compiling literature reviews; examples from a wide range of disciplines and a student glossary.
Variable-length Codes for Data Compression
Most data compression methods that are based on variable-length codes employ the Huffman or Golomb codes. However, there are a large number of less-known codes that have useful properties - such as those containing certain bit patterns, or those that are robust - and these can be useful. This book brings this large set of codes to the attention of workers in the field and to students of computer science.
David Salomon’s clear style of writing and presentation, which has been familiar to readers for many years now, allows easy access to this topic. This comprehensive text offers readers a detailed, reader-friendly description of the variable length codes used in the field of data compression. Readers are only required to have a general familiarity with computer methods and essentially an understanding of the representation of data in bits and files.
Topics and Features:
• Discusses codes in-depth, not the compression algorithms, which are readily available in many books
• Includes detailed illustrations, providing readers with a deeper and broader understanding of the topic
• Provides a supplementary author-maintained website, with errata and auxiliary material – www.davidsalomon.name/VLCadvertis/VLC.html
• Easily understood and used by computer science majors requiring only a minimum of mathematics
• Can easily be used as a main or auxiliary textbook for courses on algebraic codes or data compression and protection
• An ideal companion volume to David Salomon’s fourth edition of Data Compression: The Complete Reference
Computer scientists, electrical engineers and students majoring in computer science or electrical engineering will find this volume a valuable resource, as will those readers in various physical sciences and mathematics.
David Salomon is a professor emeritus of Computer Science at California State University, Northridge. He has authored numerous articles and books, including Coding for Data and Computer Communications, Guide to Data Compression Methods, Data Privacy and Security, Computer Graphics and Geometric Modeling, Foundations of Computer Security, and Transformations and Projections in Computer Graphics.
Prentice Hall - Fundamentals of Statistical Signal Processing-Estimation Theory (Kay)
A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.
Hebbian Learning and Negative Feedback Networks
The central idea of Hebbian Learning and Negative Feedback Networks is that artificial neural networks using negative feedback of activation can use simple Hebbian learning to self-organise so that they uncover interesting structures in data sets. Two variants are considered: the first uses a single stream of data to self-organise. By changing the learning rules for the network, it is shown how to perform Principal Component Analysis, Exploratory Projection Pursuit, Independent Component Analysis, Factor Analysis and a variety of topology preserving mappings for such data sets.
The second variants use two input data streams on which they self-organise. In their basic form, these networks are shown to perform Canonical Correlation Analysis, the statistical technique which finds those filters onto which projections of the two data streams have greatest correlation.
The book encompasses a wide range of real experiments and displays how the approaches it formulates can be applied to the analysis of real problems.
Pattern Recognitionand Image Analysis 4th Iberian Conference, IbPRIA 2009
This volume constitutes the refereed proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2009, held in Póvoa de Varzim, Portugal in June 2009. The 33 revised full papers and 29 revised poster papers presented together with 3 invited talks were carefully reviewed and selected from 106 submissions. The papers are organized in topical sections on computer vision, image analysis and processing, as well as pattern recognition.
CRC Press Pattern Recognition in Speech and Language Processing
Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field.
Pattern Recognition in Speech and Language Processing offers a systematic, up-to-date presentation of these recent developments. It begins with the fundamentals and recent theoretical advances in pattern recognition, with emphasis on classifier design criteria and optimization procedures. The focus then shifts to the application of these techniques to speech processing, with chapters exploring advances in applying pattern recognition to real speech and audio processing systems. The final section of the book examines topics related to pattern recognition in language processing: topics that represent promising new trends with direct impact on information processing systems for the Web, broadcast news, and other content-rich information resources.
Each self-contained chapter includes figures, tables, diagrams, and references. The collective effort of experts at the forefront of the field, Pattern Recognition in Speech and Language Processing offers in-depth, insightful discussions on new developments and contains a wealth of information integral to the further development of human-machine communications.
Four Short Courses on Harmonic Analysis
This state-of-the-art textbook examines four research directions in harmonic analysis and features some of the latest applications in the field, including cosmic microwave background analysis, human cortex image denoising, and wireless communication. The work is the first one that combines spline theory (from a numerical or approximation-theoretical view), wavelets, frames, and time-frequency methods leading up to a construction of wavelets on manifolds other.
Topics covered:
* Frames and bases in mathematics and engineering
* Wavelets with composite dilations and their applications
* Wavelets on the sphere and their applications
* Wiener's Lemma: theme and variations
2D Object Detection and Recognition Models, Algorithms, and Networks
Two important subproblems of computer vision are the detection and recognition of 2D objects in gray-level images. This book discusses the construction and training of models, computational approaches to efficient implementation, and parallel implementations in biologically plausible neural network architectures. The approach is based on statistical modeling and estimation, with an emphasis on simplicity, transparency, and computational efficiency. The book describes a range of deformable template models, from coarse sparse models involving discrete, fast computations to more finely detailed models based on continuum formulations, involving intensive optimization. Each model is defined in terms of a subset of points on a reference grid (the template), a set of admissible instantiations of these points (deformations), and a statistical model for the data given a particular instantiation of the object present in the image. A recurring theme is a coarse to fine approach to the solution of vision problems. The book provides detailed descriptions of the algorithms used as well as the code, and the software and data sets are available on the Web.
IET - Video Compression Systems
Digital video compression has revolutionised the broadcast industry. Its implementation has been the vital key to the expansion of video via satellite, cable, internet and terrestrial TV. However, new technologies not only enable new applications, they also create new challenges such as how to measure video quality, and how to maintain video quality in concatenated compression systems. Video Compression Systems provides an overview on many issues concerning today's complex digital video systems: from video quality measurements to statistical multiplexing, from pre-processing to transcoding and concatenation.
It explains video compression systems from first principles and gives a detailed summary of currently used MPEG standards, as well as non-MPEG algorithms. Furthermore, it provides a summary of motion estimation algorithms and explains processing priorities for mobile applications, HDTV, contribution and distribution systems, as well as for end user systems. Video Compression Systems focuses intentionally on the principles rather than the mathematics in order to make it more readable and accessible to a wider audience.
It is aimed at senior undergraduate students taking modules in video technologies, multimedia processing or video compression, as well as television engineers working on video compression systems.
A Wavelet Tour of Signal Processing: the Sparse Way
The new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today’s signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications.
Horn & Johnson, Topics in Matrix Analysis (CUP 1991)
Building on the foundations of its predecessor volume, Matrix Analysis, this book treats in detail several topics with important applications and of special mathematical interest in matrix theory not included in the previous text. These topics include the field of values, stable matrices and inertia, singular values, matrix equations and Kronecker products, Hadamard products, and matrices and functions. The authors assume a background in elementary linear algebra and knowledge of rudimentary analytical concepts. The book should be welcomed by graduate students and researchers in a variety of mathematical fields both as an advanced text and as a modern reference work.
Horn R A, Johnson C R, Matrix Analysis (CUP 1990)
Linear algebra and matrix theory have long been fundamental tools in mathematical disciplines as well as fertile fields for research. In this book the authors present classical and recent results of matrix analysis that have proved to be important to applied mathematics. Facts about matrices, beyond those found in an elementary linear algebra course, are needed to understand virtually any area of mathematical science, but the necessary material has appeared only sporadically in the literature and in university curricula. As interest in applied mathematics has grown, the need for a text and reference offering a broad selection of topics in matrix theory has become apparent, and this book meets that need. This volume reflects two concurrent views of matrix analysis. First, it encompasses topics in linear algebra that have arisen out of the needs of mathematical analysis. Second, it is an approach to real and complex linear algebraic problems that does not hesitate to use notions from analysis. Both views are reflected in its choice and treatment of topics.
Wavelet Theory and Its Application to Pattern Recognition World
This is not a purely mathematical text. It presents the basic principle of wavelet theory to electrical and electronic engineers, computer scientists, and students, as well as the ideas of how wavelets can be applied to pattern recognition. It also contains many research results from the authors' research team.
Mathematical Foundations of Infinite-dimensional Statistical Models
In nonparametric and high-dimensional statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, on approximation and wavelet theory, and on the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is then presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions.
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing. The theoretical and numerical foundations are tackled before the applications are discussed. Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more. The presentation is elegant and engaging.
Sparse and Redundant Representations is intended for graduate students in applied mathematics and electrical engineering, as well as applied mathematicians, engineers, and researchers who are active in the fields of signal and image processing.
Dimension Reduction:A Guided Tour
We give a tutorial overview of several geometric methods for dimension
reduction. We divide the methods into projective methods and methods
that model the manifold on which the data lies. For projective
methods, we review projection pursuit, principal component analysis
(PCA), kernel PCA, probabilistic PCA, canonical correlation analysis,
oriented PCA, and several techniques for sufficient dimension reduction.
For the manifold methods, we review multidimensional scaling
(MDS), landmark MDS, Isomap, locally linear embedding, Laplacian
eigenmaps and spectral clustering. The Nystr¨om method, which links
several of the manifold algorithms, is also reviewed. The goal is to provide
a self-contained overview of key concepts underlying many of these
algorithms, and to give pointers for further reading.
Riemannian Geometry and Statistical Machine Learning
Statistical machine learning algorithms deal with the problem of selecting an appropriate
statistical model from a model space based on a training set {xi}N
i=1 ⊂ X or {(xi, yi)}N
i=1 ⊂ X × Y. In doing so they either implicitly or explicitly make assumptions on the geometries
of the model space and the data space X. Such assumptions are crucial to the success of
the algorithms as different geometries are appropriate for different models and data spaces.
By studying these assumptions we are able to develop new theoretical results that enhance our
understanding of several popular learning algorithms. Furthermore, using geometrical reasoning
we are able to adapt existing algorithms such as radial basis kernels and linear margin classifiers
to non-Euclidean geometries. Such adaptation is shown to be useful when the data space does
not exhibit Euclidean geometry. In particular, we focus in our experiments on the space of text
documents that is naturally associated with the Fisher information metric on corresponding
multinomial models.
GEOMETRIC PARTIAL DIFFERENTIAL EQUATIONS AND IMAGE ANALYSIS
This book provides an introduction to the use of geometric partial differential equations in image processing and computer vision. It brings a number of new concepts into the field, providing a very fundamental and formal approach to image processing. State-of-the-art practical results in a large number of real problems are achieved with the techniques described. Applications covered include image segmentation, shape analysis, image enhancement, and tracking. The volume provides information for people investigating new solutions to image processing problems as well as for people searching for existent advanced solutions.
Mathematica in Action: Problem Solving Through Visualization and Computation
In this third edition of Mathematica in Action, award-winning author Stan Wagon guides beginner and veteran users alike through Mathematica's powerful tools for mathematical exploration. The transition to Mathematica 7 is made smooth with plenty of examples and case studies that utilize Mathematica's newest tools, such as dynamic manipulations and adaptive three-dimensional plotting. Mathematica in Action also emphasizes the breadth of Mathematica and the impressive results of combining techniques from different areas. This material enables the reader to use Mathematica to solve a variety of complex problems of mathematics.
Case studies ranging from elementary to sophisticated are provided throughout. Whenever possible, the book shows how Mathematica can be used to discover new things. Striking examples include the design of a road on which a square wheel bike can ride, the design of a drill that can drill square holes, an illustration of the Banach-Tarski Paradox via hyperbolic geometry, new and surprising formulas for p, the discovery of shadow orbits for chaotic systems, and the use of powerful new capabilities for three-dimensional graphics. Visualization is emphasized throughout, with finely crafted graphics in each chapter. All Mathematica code is included on a CD, saving the reader hours of typing.
Wagon is the author of nine books on mathematics, including A Course in Computational Number Theory, named one of the ten best math books of 2000 by the American Library Association. He has written extensively on the educational applications of Mathematica, including the books VisualDSolve: Visualizing Differential Equations with Mathematica, and Animating Calculus: Mathematica Notebooks for the Laboratory.
Moments and Moment Invariants in Pattern Recognition
Moments as projections of an image’s intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging.
Key features:
Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments.
Considers invariants to traditional transforms – translation, rotation, scaling, and affine transform - from a new point of view, which offers new possibilities of designing optimal sets of invariants.
Reviews and extends a recent field of invariants with respect to convolution/blurring.
Introduces implicit moment invariants as a tool for recognizing elastically deformed objects.
Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments.
Offers comprehensive advice on the construction of various invariants illustrated with practical examples.
Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course.
Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical i
Nonlinear operators in image restoration
We firstly present a variational approach such that during image restoration, edges detected in the original image are being preserved, and then we compare in a second part, the mathematical foundation of this method with respect to some of the well known methods recently proposed in the literature within the class of PDE based algorithms (anisotropic diffusion, mean curvature motion, min/max flow technique,...).
The performance of our approach is carefully examined and compared to the classical methods. Experimental results on synthetic and real images will illustrate the capabilities of all the studied approaches.
Machine Learning for Human Motion Analysis Theory and Practice
"With the ubiquitous presence of video data and its increasing importance in a wide range of real-world applications, it is becoming increasingly necessary to automatically analyze and interpret object motions from large quantities of footage.
Machine Learning for Human Motion Analysis: Theory and Practice highlights the development of robust and effective vision-based motion understanding systems. This advanced publication addresses a broad audience including practicing professionals working with specific vision applications such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval."
Algebraic Geometry and Statistical Learning Theory
Sure to be influential, Watanabe's book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.
Computer Vision (Shapiro 2000)
A textbook and reference for students and practitioners, presenting the necessary theory for work in fields where significant information must be extracted from images. Topics covered include databases and virtual and augmented reality, and the text includes more than 250 exercises and programming projects. DLC: Computer vision.
Effective C# (Covers C# 4.0) 50 Specific Ways to Improve Your C# 2nd Edition
C# has matured over the past decade: It’s now a rich language with generics, functional programming concepts, and support for both static and dynamic typing. This palette of techniques provides great tools for many different idioms, but there are also many ways to make mistakes. In Effective C#, Second Edition, respected .NET expert Bill Wagner identifies fifty ways you can leverage the full power of the C# 4.0 language to express your designs concisely and clearly.
Effective C#, Second Edition, follows a clear format that makes it indispensable to hundreds of thousands of developers: clear, practical explanations, expert tips, and plenty of realistic code examples. Drawing on his unsurpassed C# experience, Wagner addresses everything from types to resource management to dynamic typing to multicore support in the C# language and the .NET framework. Along the way, he shows how to avoid common pitfalls in the C# language and the .NET environment. You’ll learn how to
Use both types of C# constants for efficiency and maintainability (see Item 2)
Employ immutable data types to promote multicore processing (see Item 20)
Minimize garbage collection, boxing, and unboxing (see Items 16 and 45)
Take full advantage of interfaces and delegates (see Items 22 though 25)
Make the most of the parallel framework (see Items 35 through 37)
Use duck typing in C# (see Item 38)
Spot the advantages of the dynamic and Expression types over reflection (see Items 42 and 43)
Assess why query expressions are better than loops (see Item 8)
Understand how generic covariance and contravariance affect your designs (see Item 29)
See how optional parameters can minimize the number of method overloads (see Item 10)
You’re already a successful C# programmer–this book will help you become an outstanding one.
Vision with Direction - A Systematic Introduction to Image Processing and Computer Vision
This introductory textbook presents the modern signal processing concepts used in computer vision and image analysis in a systematic and mathematically coherent way. For the first time in a textbook on image processing, single direction, group direction, corners and edges, Hough transform, and motion estimation are developed in a principled way using direction tensors as the unifying concept.
The topics presented include Hilbert spaces, the Fourier transform, scale analysis, direction fields, structure tensors, motion tensors, the Hough transform, grouping, and segmentation. Directional signal processing, an increasingly crucial element of computer vision for which neural circuits exist in human vision, is dealt with in depth by use of tensors. All chapters are richly illustrated, with color graphics from cover to cover; applications are studied in various fields, including biometric person authentication, texture analysis, optical character recognition, and motion estimation and tracking; and exercises help the sudent verify progress.
Developed out of courses given by the author, this introductory textbook addresses advanced undergarduates as well as master and PhD students in computer science, engineering, mathematics, and in other disciplines where techniques from computer vision, image processing, visual computation and signal analysis are applied.