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نویسندگان: Adrian Kaehler. Gary Bradski
سری:
ISBN (شابک) : 1491937998, 9781491937990
ناشر: O’Reilly Media
سال نشر: 2017
تعداد صفحات: 0
زبان: English
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 17 مگابایت
کلمات کلیدی مربوط به کتاب یادگیری OpenCV 3: چشم انداز رایانه در C ++ با کتابخانه OpenCV: بینایی و تشخیص الگوی کامپیوتر، هوش مصنوعی و یادگیری ماشین، علوم کامپیوتر، کامپیوتر و فناوری، رباتیک، علوم کامپیوتر، کامپیوتر و فناوری، آموزشها، C & C++، زبانهای برنامهنویسی، کامپیوتر و فناوری، C، C و C ++، زبانهای برنامهنویسی، کامپیوتر و فناوری، C++، C و C++، زبانهای برنامهنویسی، کامپیوتر و فناوری، رباتیک و اتوماسیون، صنعتی، تولید و سیستمهای عملیاتی، مهندسی، مهندسی و حملونقل، زبانهای برنامهنویسی، علوم کامپیوتر، کتاب درسی جدید، مستعمل و اجاره
در صورت تبدیل فایل کتاب Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب یادگیری OpenCV 3: چشم انداز رایانه در C ++ با کتابخانه OpenCV نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
با این راهنمای عملی، در حوزه بینایی کامپیوتری که به سرعت در حال گسترش است شروع کنید. این کتاب توسط آدریان کاهلر و گری برادسکی، خالق کتابخانه OpenCV منبع باز نوشته شده است، این کتاب مقدمه ای کامل برای توسعه دهندگان، دانشگاهیان، روباتیک ها و علاقمندان ارائه می دهد. شما یاد خواهید گرفت که برای ساختن برنامههایی که رایانهها را قادر به \"دیدن\" و تصمیمگیری بر اساس آن دادهها میکنند، چه چیزی لازم است.
با بیش از 500 عملکردی که حوزههای بینایی زیادی را در بر میگیرد، OpenCV برای تجارت استفاده میشود. کاربردهایی مانند امنیت، تصویربرداری پزشکی، تشخیص الگو و چهره، روباتیک و بازرسی محصولات کارخانه. این کتاب به شما یک پایه محکم در بینایی کامپیوتر و OpenCV برای ساخت برنامه های بینایی ساده یا پیچیده می دهد. تمرینهای عملی در هر فصل به شما کمک میکند تا آنچه را که آموختهاید به کار ببندید.
این جلد، کل کتابخانه را در اجرای مدرن C++ خود، از جمله ابزارهای یادگیری ماشین برای بینایی کامپیوتر، پوشش میدهد.
Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to "see" and make decisions based on that data.
With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.
This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.
Copyright Table of Contents Preface Purpose of This Book Who This Book Is For What This Book Is Not About the Programs in This Book Prerequisites How This Book Is Best Used Conventions Used in This Book Using Code Examples O’Reilly Safari We’d Like to Hear from You Acknowledgments Thanks for Help on OpenCV Thanks for Help on This Book Adrian Adds... Gary Adds... Chapter 1. Overview What Is OpenCV? Who Uses OpenCV? What Is Computer Vision? The Origin of OpenCV OpenCV Block Diagram Speeding Up OpenCV with IPP Who Owns OpenCV? Downloading and Installing OpenCV Installation Getting the Latest OpenCV via Git More OpenCV Documentation Supplied Documentation Online Documentation and the Wiki OpenCV Contribution Repository Downloading and Building Contributed Modules Portability Summary Exercises Chapter 2. Introduction to OpenCV Include Files Resources First Program—Display a Picture Second Program—Video Moving Around A Simple Transformation A Not-So-Simple Transformation Input from a Camera Writing to an AVI File Summary Exercises Chapter 3. Getting to Know OpenCV Data Types The Basics OpenCV Data Types Overview of the Basic Types Basic Types: Getting Down to Details Helper Objects Utility Functions The Template Structures Summary Exercises Chapter 4. Images and Large Array Types Dynamic and Variable Storage The cv::Mat Class: N-Dimensional Dense Arrays Creating an Array Accessing Array Elements Individually The N-ary Array Iterator: NAryMatIterator Accessing Array Elements by Block Matrix Expressions: Algebra and cv::Mat Saturation Casting More Things an Array Can Do The cv::SparseMat Class: Sparse Arrays Accessing Sparse Array Elements Functions Unique to Sparse Arrays Template Structures for Large Array Types Summary Exercises Chapter 5. Array Operations More Things You Can Do with Arrays cv::abs() cv::absdiff() cv::add() cv::addWeighted() cv::bitwise_and() cv::bitwise_not() cv::bitwise_or() cv::bitwise_xor() cv::calcCovarMatrix() cv::cartToPolar() cv::checkRange() cv::compare() cv::completeSymm() cv::convertScaleAbs() cv::countNonZero() cv::cvarrToMat() cv::dct() cv::dft() cv::cvtColor() cv::determinant() cv::divide() cv::eigen() cv::exp() cv::extractImageCOI() cv::flip() cv::gemm() cv::getConvertElem() and cv::getConvertScaleElem() cv::idct() cv::idft() cv::inRange() cv::insertImageCOI() cv::invert() cv::log() cv::LUT() cv::magnitude() cv::Mahalanobis() cv::max() cv::mean() cv::meanStdDev() cv::merge() cv::min() cv::minMaxIdx() cv::minMaxLoc() cv::mixChannels() cv::mulSpectrums() cv::multiply() cv::mulTransposed() cv::norm() cv::normalize() cv::perspectiveTransform() cv::phase() cv::polarToCart() cv::pow() cv::randu() cv::randn() cv::randShuffle() cv::reduce() cv::repeat() cv::scaleAdd() cv::setIdentity() cv::solve() cv::solveCubic() cv::solvePoly() cv::sort() cv::sortIdx() cv::split() cv::sqrt() cv::subtract() cv::sum() cv::trace() cv::transform() cv::transpose() Summary Exercises Chapter 6. Drawing and Annotating Drawing Things Line Art and Filled Polygons Fonts and Text Summary Exercises Chapter 7. Functors in OpenCV Objects That “Do Stuff” Principal Component Analysis (cv::PCA) Singular Value Decomposition (cv::SVD) Random Number Generator (cv::RNG) Summary Exercises Chapter 8. Image, Video, and Data Files HighGUI: Portable Graphics Toolkit Working with Image Files Loading and Saving Images A Note About Codecs Compression and Decompression Working with Video Reading Video with the cv::VideoCapture Object Writing Video with the cv::VideoWriter Object Data Persistence Writing to a cv::FileStorage Reading from a cv::FileStorage cv::FileNode Summary Exercises Chapter 9. Cross-Platform and Native Windows Working with Windows HighGUI Native Graphical User Interface Working with the Qt Backend Integrating OpenCV with Full GUI Toolkits Summary Exercises Chapter 10. Filters and Convolution Overview Before We Begin Filters, Kernels, and Convolution Border Extrapolation and Boundary Conditions Threshold Operations Otsu’s Algorithm Adaptive Threshold Smoothing Simple Blur and the Box Filter Median Filter Gaussian Filter Bilateral Filter Derivatives and Gradients The Sobel Derivative Scharr Filter The Laplacian Image Morphology Dilation and Erosion The General Morphology Function Opening and Closing Morphological Gradient Top Hat and Black Hat Making Your Own Kernel Convolution with an Arbitrary Linear Filter Applying a General Filter with cv::filter2D() Applying a General Separable Filter with cv::sepFilter2D Kernel Builders Summary Exercises Chapter 11. General Image Transforms Overview Stretch, Shrink, Warp, and Rotate Uniform Resize Image Pyramids Nonuniform Mappings Affine Transformation Perspective Transformation General Remappings Polar Mappings LogPolar Arbitrary Mappings Image Repair Inpainting Denoising Histogram Equalization cv::equalizeHist(): Contrast equalization Summary Exercises Chapter 12. Image Analysis Overview Discrete Fourier Transform cv::dft(): The Discrete Fourier Transform cv::idft(): The Inverse Discrete Fourier Transform cv::mulSpectrums(): Spectrum Multiplication Convolution Using Discrete Fourier Transforms cv::dct(): The Discrete Cosine Transform cv::idct(): The Inverse Discrete Cosine Transform Integral Images cv::integral() for Standard Summation Integral cv::integral() for Squared Summation Integral cv::integral() for Tilted Summation Integral The Canny Edge Detector cv::Canny() Hough Transforms Hough Line Transform Hough Circle Transform Distance Transformation cv::distanceTransform() for Unlabeled Distance Transform cv::distanceTransform() for Labeled Distance Transform Segmentation Flood Fill Watershed Algorithm Grabcuts Mean-Shift Segmentation Summary Exercises Chapter 13. Histograms and Templates Histogram Representation in OpenCV cv::calcHist(): Creating a Histogram from Data Basic Manipulations with Histograms Histogram Normalization Histogram Threshold Finding the Most Populated Bin Comparing Two Histograms Histogram Usage Examples Some More Sophisticated Histograms Methods Earth Mover’s Distance Back Projection Template Matching Square Difference Matching Method (cv::TM_SQDIFF) Normalized Square Difference Matching Method (cv::TM_SQDIFF_NORMED) Correlation Matching Methods (cv::TM_CCORR) Normalized Cross-Correlation Matching Method (cv::TM_CCORR_NORMED) Correlation Coefficient Matching Methods (cv::TM_CCOEFF) Normalized Correlation Coefficient Matching Method (cv::TM_CCOEFF_NORMED) Summary Exercises Chapter 14. Contours Contour Finding Contour Hierarchies Drawing Contours A Contour Example Another Contour Example Fast Connected Component Analysis More to Do with Contours Polygon Approximations Geometry and Summary Characteristics Geometrical Tests Matching Contours and Images Moments More About Moments Matching and Hu Moments Using Shape Context to Compare Shapes Summary Exercises Chapter 15. Background Subtraction Overview of Background Subtraction Weaknesses of Background Subtraction Scene Modeling A Slice of Pixels Frame Differencing Averaging Background Method Accumulating Means, Variances, and Covariances A More Advanced Background Subtraction Method Structures Learning the Background Learning with Moving Foreground Objects Background Differencing: Finding Foreground Objects Using the Codebook Background Model A Few More Thoughts on Codebook Models Connected Components for Foreground Cleanup A Quick Test Comparing Two Background Methods OpenCV Background Subtraction Encapsulation The cv::BackgroundSubtractor Base Class KaewTraKuPong and Bowden Method Zivkovic Method Summary Exercises Chapter 16. Keypoints and Descriptors Keypoints and the Basics of Tracking Corner Finding Introduction to Optical Flow Lucas-Kanade Method for Sparse Optical Flow Generalized Keypoints and Descriptors Optical Flow, Tracking, and Recognition How OpenCV Handles Keypoints and Descriptors, the General Case Core Keypoint Detection Methods Keypoint Filtering Matching Methods Displaying Results Summary Exercises Chapter 17. Tracking Concepts in Tracking Dense Optical Flow The Farnebäck Polynomial Expansion Algorithm The Dual TV-L1 Algorithm The Simple Flow Algorithm Mean-Shift and Camshift Tracking Mean-Shift Camshift Motion Templates Estimators The Kalman Filter A Brief Note on the Extended Kalman Filter Summary Exercises Chapter 18. Camera Models and Calibration Camera Model The Basics of Projective Geometry Rodrigues Transform Lens Distortions Calibration Rotation Matrix and Translation Vector Calibration Boards Homography Camera Calibration Undistortion Undistortion Maps Converting Undistortion Maps Between Representations with cv::convertMaps() Computing Undistortion Maps with cv::initUndistortRectifyMap() Undistorting an Image with cv::remap() Undistortion with cv::undistort() Sparse Undistortion with cv::undistortPoints() Putting Calibration All Together Summary Exercises Chapter 19. Projection and Three-Dimensional Vision Projections Affine and Perspective Transformations Bird’s-Eye-View Transform Example Three-Dimensional Pose Estimation Pose Estimation from a Single Camera Stereo Imaging Triangulation Epipolar Geometry The Essential and Fundamental Matrices Computing Epipolar Lines Stereo Calibration Stereo Rectification Stereo Correspondence Stereo Calibration, Rectification, and Correspondence Code Example Depth Maps from Three-Dimensional Reprojection Structure from Motion Fitting Lines in Two and Three Dimensions Summary Exercises Chapter 20. The Basics of Machine Learning in OpenCV What Is Machine Learning? Training and Test Sets Supervised and Unsupervised Learning Generative and Discriminative Models OpenCV ML Algorithms Using Machine Learning in Vision Variable Importance Diagnosing Machine Learning Problems Legacy Routines in the ML Library K-Means Mahalanobis Distance Summary Exercises Chapter 21. StatModel: The Standard Model for Learning in OpenCV Common Routines in the ML Library Training and the cv::ml::TrainData Structure Prediction Machine Learning Algorithms Using cv::StatModel Naïve/Normal Bayes Classifier Binary Decision Trees Boosting Random Trees Expectation Maximization K-Nearest Neighbors Multilayer Perceptron Support Vector Machine Summary Exercises Chapter 22. Object Detection Tree-Based Object Detection Techniques Cascade Classifiers Supervised Learning and Boosting Theory Learning New Objects Object Detection Using Support Vector Machines Latent SVM for Object Detection The Bag of Words Algorithm and Semantic Categorization Summary Exercises Chapter 23. Future of OpenCV Past and Present OpenCV 3.x How Well Did Our Predictions Go Last Time? Future Functions Current GSoC Work Community Contributions OpenCV.org Some AI Speculation Afterword Appendix A. Planar Subdivisions Delaunay Triangulation, Voronoi Tesselation Creating a Delaunay or Voronoi Subdivision Navigating Delaunay Subdivisions Usage Examples Exercises Appendix B. opencv_contrib An Overview of the opencv_contrib Modules Contents of opencv_contrib Appendix C. Calibration Patterns Calibration Patterns Used by OpenCV Bibliography Index About the Authors Colophon