An Introduction to 3D Computer Vision Techniques and Algorithms

An Introduction to 3D Computer Vision Techniques and Algorithms
Boguslaw Cyganek | 2009-01-01 00:00:00 | Wiley | 504 | Algorithms
Product Description
Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model.

This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently:

* present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms;
* explain scale-space vision, as well as space reconstruction and multiview integration;
* demonstrate a variety of practical applications for 3D surface imaging and analysis;
* provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures.

An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

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Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model.

This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently:

* present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms
* explain scale-space vision, as well as space reconstruction and multiview integration
* demonstrate a variety of practical applications for 3D surface imaging and analysis
* provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures

An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

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Product Details

* Hardcover: 504 pages
* Publisher: Wiley (March 3, 2009)
* Language: English
* ISBN-10: 047001704X
* ISBN-13: 978-0470017043
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Genetic Algorithms - Principles and Perspectives: A Guide to GA Theory

Genetic Algorithms - Principles and Perspectives: A Guide to GA Theory
Colin R. Reeves and Jonathan E. Rowe | 1900-01-01 00:00:00 | Springer; 1 edition | 344 | Algorithms
Genetic Algorithms (GAs) have become a highly effective tool for solving hard optimization problems. As their popularity has increased, the number of GA applications has grown in more than equal measure. Genetic Algorithm theory, however, has not kept pace with the growing use and application of GAs. Most book-length treatments of GAs provide only a cursory discussion of theory and this discussion primarily focuses on the traditional view, which depends heavily on the concept of a "schema". Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems. The authors have worked hard to make the book as accessible as possible for students and researchers. An undergraduate-level mathematical understanding of linear algebra and stochastic processes is assumed. For those readers who have not encountered GAs before, a comprehensive survey of GA concepts is provided and the variety of ways in which GAs can be implemented is outlined. Exercises are provided at the ends of the chapters with the express purpose of aiding understanding of the concepts discussed and to whet the reader's appetite for pursuing theoretical research in GAs.
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Reliable Implementation of Real Number Algorithms: Theory and Practice: International Seminar Dagstuhl Castle, Germany, January 8-13, 2006, Revised Papers ... Computer Science and General Issues)

Reliable Implementation of Real Number Algorithms: Theory and Practice: International Seminar Dagstuhl Castle, Germany, January 8-13, 2006, Revised Papers ... Computer Science and General Issues)
Peter Hertling,Christoph M. Hoffmann,Wolfram Luther,Nathalie Revol | 2008-10-07 00:00:00 | Springer | 239 | Algorithms

This book constitutes the revised papers of the International Seminar on Reliable Implementation of Real Number Algorithms, held at Dagstuhl Castle, Germany, in January 2006.

The Seminar was intended to stimulate an exchange of ideas between the different communities that deal with the problem of reliable implementation of real number algorithms. Topics included formal proofs, software libraries, systems and platforms, as well as computational geometry and solid modelling.

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Algorithmic Information Theory

Algorithmic Information Theory
Gregory. J. Chaitin | 2003-04-02 00:00:00 | IBM | 236 | Algorithms
(a) The mass of text required to describe an object;
(b) The volume of intermediate data which a computational process
would need to generate;
(c) The time for which such a process will need to execute, either on a standard \serial" computer or on computational structures unrestricted in the degree of parallelism which they can employ.
Of these three resource classes, the rst is relatively static, and pertains to the fundamental question of object describability; the others are dynamic since they relate to the resources required for a computation to execute. It is with the rst kind of resource that this book is concerned. The crucial fact here is that there exist symbolic objects (i.e., texts) which are \algorithmically inexplicable," i.e., cannot be specied by any text shorter than themselves. Since texts of this sort have the properties associated with the random sequences of classical
probability theory, the theory of describability developed in Part II of the present work yields a very interesting new view of the notion of randomness.
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Numerical Methods, Algorithms and Tools in C#

Numerical Methods, Algorithms and Tools in C#
Waldemar Dos Passos | 2009-01-01 00:00:00 | CRC Press | 600 | Algorithms
Although C, C++, Java, and Fortran are well-established programming languages, the relatively new C# is much easier to use for solving complex scientific and engineering problems. Numerical Methods, Algorithms and Tools in C# presents a broad collection of practical, ready-to-use mathematical routines employing the exciting, easy-to-learn C# programming language from Microsoft.

The book focuses on standard numerical methods, novel object-oriented techniques, and the latest Microsoft .NET programming environment. It covers complex number functions, data sorting and searching algorithms, bit manipulation, interpolation methods, numerical manipulation of linear algebraic equations, and numerical methods for calculating approximate solutions of non-linear equations. The author discusses alternative ways to obtain computer-generated pseudo-random numbers and real random numbers generated by naturally occurring physical phenomena. He also describes various methods for approximating integrals and special functions, routines for performing statistical analyses of data, and least squares and numerical curve fitting methods for analyzing experimental data, along with numerical methods for solving ordinary and partial differential equations. The final chapter offers optimization methods for the minimization or maximization of functions.

Exploiting the useful features of C#, this book shows how to write efficient, mathematically intense object-oriented computer programs. The vast array of practical examples presented can be easily customized and implemented to solve complex engineering and scientific problems typically found in real-world computer applications.
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Astrophysical Disks: Collective and Stochastic Phenomena (Astrophysics and Space Science Library)

Astrophysical Disks: Collective and Stochastic Phenomena (Astrophysics and Space Science Library)
Aleksey M. Fridman,M.Y. Marov,Ilya G. Kovalenko | 2006-07-28 00:00:00 | Springer | 351 | Algorithms

The book deals with collective and stochastic processes in astrophysical disks involving theory, observations, and the results of modelling. Among others, it examines the spiral-vortex structure in galactic and accretion disks, stochastic and ordered structures in the developed turbulence. It also describes sources of turbulence in the accretion disks, internal structure of disk in the vicinity of a black hole, numerical modelling of Be envelopes in binaries, gaseous disks in spiral galaxies with shock waves formation, observation of accretion disks in a binary system and mass distribution of luminous matter in disk galaxies.

The editors adeptly brought together collective and stochastic phenomena in the modern field of astrophysical disks, their formation, structure, and evolution involving the methodology to deal with, the results of observation and modelling, thereby advancing the study in this important branch of astrophysics and benefiting professional researchers, lecturers, and graduate students.

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Handbook for Matrix Computations (Frontiers in Applied Mathematics)

Handbook for Matrix Computations (Frontiers in Applied Mathematics)
Charles Van Loan,Thomas F. Coleman | 1987-01-01 00:00:00 | Society for Industrial Mathematics | 272 | Algorithms
Provides the user with a step-by-step introduction to Fortran 77, BLAS, LINPACK, and MATLAB. It is a reference that spans several levels of practical matrix computations with a strong emphasis on examples and "hands on" experience.

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