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Top opencv books mentioned on stackoverflow.com

The C Programming Language

Brian W. Kernighan, Dennis M. Ritchie

Introduces the features of the C programming language, discusses data types, variables, operators, control flow, functions, pointers, arrays, and structures, and looks at the UNIX system interface

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Learning OpenCV

Gary Bradski, Adrian Kaehler

Describes the features and functions of OpenCV along with information on how to build applications using computer vision.

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Digital Image Processing

Rafael C. Gonzalez, Richard Eugene Woods

A comprehensive digital image processing book that reflects new trends in this field such as document image compression and data compression standards. The book includes a complete rewrite of image data compression, a new chapter on image analysis, and a new section on image morphology.

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Pattern Recognition and Machine Learning

Christopher M. Bishop

The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.

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Multiple View Geometry in Computer Vision

Richard Hartley, Andrew Zisserman

A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.

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Computer Vision

David Forsyth, Jean Ponce

The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

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Data Mining

Ian H. Witten, Eibe Frank, Mark Andrew Hall

Provides information on the tools and techniques of data mining, covering such topics as data transformation, ensemble learning, and datasets, and presents instructions on the Weka machine learning software.

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Learning OpenCV

Gary Bradski, Adrian Kaehler

Describes the features and functions of OpenCV along with information on how to build applications using computer vision. Original.

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Digital Image Processing

Rafael C. Gonzalez, Richard Eugene Woods

For courses in Image Processing and Computer Vision. Completely self-contained and heavily illustrated this introduction to basic concepts and methodologies for digital image processing is written at a level that truly is suitable for seniors and first-year graduate students in almost any technical discipline. The leading textbook in its field for more than twenty years, it continues its cutting-edge focus on contemporary developments in all mainstream areas of image processing e.g., image fundamentals, image enhancement in the spatial and frequency domains, restoration, color image processing, wavelets, image compression, morphology, segmentation, image description, and the fundamentals of object recognition. It focuses on material that is fundamental and has a broad scope of application.

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Digital Image Processing

Rafael C. Gonzalez

A comprehensive digital image processing book that reflects new trends in this field such as document image compression and data compression standards. The book includes a complete rewrite of image data compression, a new chapter on image analysis, and a new section on image morphology.

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Pro Android Augmented Reality

Raghav Sood

Augmented reality (AR) offers a live direct or indirect view of a physical, real-world environment, where the elements and surroundings are augmented by computer-generated sensory input such as graphics and GPS data. It makes a game more real. Your social media app puts you where want to be or go. Pro Android Augmented Reality walks you through the foundations of building an augmented reality application. From using various software and Android hardware sensors, such as an accelerometer or a magnetometer (compass), you'll learn the building blocks of augmented reality for both marker- and location-based apps. Case studies are included in this one-of-a-kind book, which pairs nicely with other Android development books. After reading Pro Android Augmented Reality, you'll be able to build augmented reality rich media apps or integrate all the best augmented reality into your favorite Android smartphone and/or tablet. What you’ll learn How to use most Android cameras How to find the user's location with GPS data How to detect movement and orientation of the device How to program against the accelerometer and compass How to use the AndAR library in marker recognition How to create an artificial horizon for your app How to integrate the Google Maps API into AR apps How to build enterprise augmented reality apps using the case studies in this book Who this book is for This book is for Android developers familiar with Android programming, but new to the camera, accelerometer, magnetometer and building augmented reality applications in general. Table of Contents Applications of Augmented Reality Basics of Augmented Reality on the Android Platform Adding Overlays Artificial Horizons Other Features of Augmented Reality A Simple App Using AR A More Complex Project Using More AR Features A Project Using All AR Features An AR Game

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Computer Vision

David Forsyth, Jean Ponce

Computer Vision: A Modern Approach, 2e, is appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods

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Matrix Methods in Data Mining and Pattern Recognition

Lars Eldén

Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB®. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed by the author are: classification of handwritten digits, text mining, text summarization, pagerank computations related to the GoogleÔ search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.Audience The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.Contents Preface; Part I: Linear Algebra Concepts and Matrix Decompositions. Chapter 1: Vectors and Matrices in Data Mining and Pattern Recognition; Chapter 2: Vectors and Matrices; Chapter 3: Linear Systems and Least Squares; Chapter 4: Orthogonality; Chapter 5: QR Decomposition; Chapter 6: Singular Value Decomposition; Chapter 7: Reduced-Rank Least Squares Models; Chapter 8: Tensor Decomposition; Chapter 9: Clustering and Nonnegative Matrix Factorization; Part II: Data Mining Applications. Chapter 10: Classification of Handwritten Digits; Chapter 11: Text Mining; Chapter 12: Page Ranking for a Web Search Engine; Chapter 13: Automatic Key Word and Key Sentence Extraction; Chapter 14: Face Recognition Using Tensor SVD. Part III: Computing the Matrix Decompositions. Chapter 15: Computing Eigenvalues and Singular Values; Bibliography; Index.

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Handbook of Character Recognition and Document Image Analysis

Horst Bunke, Patrick Shen-pei Wang

Optical character recognition and document image analysis have become very important areas with a fast growing number of researchers in the field. This comprehensive handbook with contributions by eminent experts, presents both the theoretical and practical aspects at an introductory level wherever possible.

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Feature Extraction and Image Processing

Mark S. Nixon, Alberto S. Aguado

* Essential reading for engineers and students working in this cutting edge field * Ideal module text and background reference for courses in image processing and computer vision * Companion website includes worksheets, links to free software, Matlab files and new demonstrations Image processing and computer vision are currently hot topics with undergraduates and professionals alike. Feature Extraction and Image Processing provides an essential guide to the implementation of image processing and computer vision techniques, explaining techniques and fundamentals in a clear and concise manner. Readers can develop working techniques, with usable code provided throughout and working Matlab and Mathcad files on the web. Focusing on feature extraction while also covering issues and techniques such as image acquisition, sampling theory, point operations and low-level feature extraction, the authors have a clear and coherent approach that will appeal to a wide range of students and professionals. The new edition includes: * New coverage of curvature in low-level feature extraction (SIFT and saliency) and features (phase congruency); geometric active contours; morphology; camera models * Updated coverage of image smoothing (anistropic diffusion); skeletonization; edge detection; curvature; shape descriptions (moments) * Essential reading for engineers and students working in this cutting edge field * Ideal module text and background reference for courses in image processing and computer vision * Companion website includes worksheets, links to free software, Matlab files and solutions

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Mastering OpenCV with Practical Computer Vision Projects

Shervin Emami, Daniel Lélis Baggio, Khvedchenia Ievgen, Naureen Mahmood

Annotation Computer Vision is fast becoming an important technology and is used in Mars robots, national security systems, automated factories, driver-less cars, and medical image analysis to new forms of human-computer interaction. OpenCV is the most common library for computer vision, providing hundreds of complex and fast algorithms. But it has a steep learning curve and limited in-depth tutorials.Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCVs new C++ interface before migrating from the C API to the C++ API.Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects youre most interested in.Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on.Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects.

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Optical Character Recognition

Stephen V. Rice, George Nagy, Thomas A. Nartker

Optical character recognition (OCR) is the most prominent and successful example of pattern recognition to date. There are thousands of research papers and dozens of OCR products. Optical Character Rcognition: An Illustrated Guide to the Frontier offers a perspective on the performance of current OCR systems by illustrating and explaining actual OCR errors. The pictures and analysis provide insight into the strengths and weaknesses of current OCR systems, and a road map to future progress. Optical Character Recognition: An Illustrated Guide to the Frontier will pique the interest of users and developers of OCR products and desktop scanners, as well as teachers and students of pattern recognition, artificial intelligence, and information retrieval. The first chapter compares the character recognition abilities of humans and computers. The next four chapters present 280 illustrated examples of recognition errors, in a taxonomy consisting of Imaging Defects, Similar Symbols, Punctuation, and Typography. These examples were drawn from large-scale tests conducted by the authors. The final chapter discusses possible approaches for improving the accuracy of today's systems, and is followed by an annotated bibliography. Optical Character Recognition: An Illustrated Guide to the Frontier is suitable as a secondary text for a graduate level course on pattern recognition, artificial intelligence, and information retrieval, and as a reference for researchers and practitioners in industry.

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Functions of Matrices

Nicholas J. Higham

“This superb book is timely and is written with great attention paid to detail, particularly in its referencing of the literature. The book has a wonderful blend of theory and code (MATLAB®) so will be useful both to nonexperts and to experts in the field.” — Alan Laub, Professor, University of California, Los Angeles The only book devoted exclusively to matrix functions, this research monograph gives a thorough treatment of the theory of matrix functions and numerical methods for computing them. The author's elegant presentation focuses on the equivalent definitions of f(A) via the Jordan canonical form, polynomial interpolation, and the Cauchy integral formula, and features an emphasis on results of practical interest and an extensive collection of problems and solutions. Functions of Matrices: Theory and Computation is more than just a monograph on matrix functions; its wide-ranging content—including an overview of applications, historical references, and miscellaneous results, tricks, and techniques with an f(A) connection—makes it useful as a general reference in numerical linear algebra.Other key features of the book include development of the theory of conditioning and properties of the Fréchet derivative; an emphasis on the Schur decomposition, the block Parlett recurrence, and judicious use of Padé approximants; the inclusion of new, unpublished research results and improved algorithms; a chapter devoted to the f(A)b problem; and a MATLAB® toolbox providing implementations of the key algorithms.Audience: This book is for specialists in numerical analysis and applied linear algebra as well as anyone wishing to learn about the theory of matrix functions and state of the art methods for computing them. It can be used for a graduate-level course on functions of matrices and is a suitable reference for an advanced course on applied or numerical linear algebra. It is also particularly well suited for self-study. Contents: List of Figures; List of Tables; Preface; Chapter 1: Theory of Matrix Functions; Chapter 2: Applications; Chapter 3: Conditioning; Chapter 4: Techniques for General Functions; Chapter 5: Matrix Sign Function; Chapter 6: Matrix Square Root; Chapter 7: Matrix pth Root; Chapter 8: The Polar Decomposition; Chapter 9: Schur-Parlett Algorithm; Chapter 10: Matrix Exponential; Chapter 11: Matrix Logarithm; Chapter 12: Matrix Cosine and Sine; Chapter 13: Function of Matrix Times Vector: f(A)b; Chapter 14: Miscellany; Appendix A: Notation; Appendix B: Background: Definitions and Useful Facts; Appendix C: Operation Counts; Appendix D: Matrix Function Toolbox; Appendix E: Solutions to Problems; Bibliography; Index.

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Portable Gui Development With C++

Mark Watson

Designed for software developers who want to maximize their investment in commercial product-development. Covers practical problems facing C++ programmers of GUI-based applications).

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