Learning OpenCV 3. Computer Vision in C++ with the OpenCV Library
- Autorzy:
- Adrian Kaehler, Gary Bradski
- Ocena:
- Bądź pierwszym, który oceni tę książkę
- Stron:
- 1024
- Dostępne formaty:
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książki
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Learning OpenCV 3. Computer Vision in C++ with the OpenCV Library
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.
- Learn OpenCV data types, array types, and array operations
- Capture and store still and video images with HighGUI
- Transform images to stretch, shrink, warp, remap, and repair
- Explore pattern recognition, including face detection
- Track objects and motion through the visual field
- Reconstruct 3D images from stereo vision
- Discover basic and advanced machine learning techniques in OpenCV
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Szczegóły książki
- ISBN Ebooka:
- 978-14-919-3800-3, 9781491938003
- Data wydania ebooka :
- 2016-12-14 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
- Język publikacji:
- 1
- Rozmiar pliku ePub:
- 42.3MB
- Rozmiar pliku Mobi:
- 82.3MB
Spis treści książki
- 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
- OReilly Safari
- Wed Like to Hear from You
- Acknowledgments
- Thanks for Help on OpenCV
- Thanks for Help on This Book
- Adrian Adds...
- Gary Adds...
- Purpose of This Book
- 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
- Windows
- Linux
- Mac OS X
- 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
- 2. Introduction to OpenCV
- Include Files
- Resources
- First ProgramDisplay a Picture
- Second ProgramVideo
- Moving Around
- A Simple Transformation
- A Not-So-Simple Transformation
- Input from a Camera
- Writing to an AVI File
- Summary
- Exercises
- Include Files
- 3. Getting to Know OpenCV Data Types
- The Basics
- OpenCV Data Types
- Overview of the Basic Types
- Basic Types: Getting Down to Details
- The point classes
- The cv::Scalar class
- The size classes
- The cv::Rect class
- The cv::RotatedRect class
- The fixed matrix classes
- The fixed vector classes
- The complex number classes
- Helper Objects
- The cv::TermCriteria class
- The cv::Range class
- The cv::Ptr template and Garbage Collection 101
- The cv::Exception class and exception handling
- The cv::DataType<> template
- The cv::InputArray and cv::OutputArray classes
- Utility Functions
- cv::alignPtr()
- cv::alignSize()
- cv::allocate()
- cv::deallocate()
- cv::fastAtan2()
- cvCeil()
- cv::cubeRoot()
- cv::CV_Assert() and CV_DbgAssert()
- cv::CV_Error() and CV_Error_()
- cv::error()
- cv::fastFree()
- cv::fastMalloc()
- cvFloor()
- cv::format()
- cv::getCPUTickCount()
- cv::getNumThreads()
- cv::getOptimalDFTSize()
- cv::getThreadNum()
- cv::getTickCount()
- cv::getTickFrequency()
- cvIsInf()
- cvIsNaN()
- cvRound()
- cv::setNumThreads()
- cv::setUseOptimized()
- cv::useOptimized()
- The Template Structures
- Summary
- Exercises
- 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
- Dynamic and Variable Storage
- 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
- More Things You Can Do with Arrays
- 6. Drawing and Annotating
- Drawing Things
- Line Art and Filled Polygons
- cv::circle()
- cv::clipLine()
- cv::ellipse()
- cv::ellipse2Poly()
- cv::fillConvexPoly()
- cv::fillPoly()
- cv::line()
- cv::rectangle()
- cv::polyLines()
- cv::LineIterator
- Fonts and Text
- cv::putText()
- cv::getTextSize()
- Line Art and Filled Polygons
- Summary
- Exercises
- Drawing Things
- 7. Functors in OpenCV
- Objects That Do Stuff
- Principal Component Analysis (cv::PCA)
- cv::PCA::PCA()
- cv::PCA::operator()()
- cv::PCA::project()
- cv::PCA::backProject()
- Singular Value Decomposition (cv::SVD)
- cv::SVD()
- cv::SVD::operator()()
- cv::SVD::compute()
- cv::SVD::solveZ()
- cv::SVD::backSubst()
- Random Number Generator (cv::RNG)
- cv::theRNG()
- cv::RNG()
- cv::RNG::operator T(), where T is your favorite type
- cv::RNG::operator()
- cv::RNG::uniform()
- cv::RNG::gaussian()
- cv::RNG::fill()
- Principal Component Analysis (cv::PCA)
- Summary
- Exercises
- Objects That Do Stuff
- 8. Image, Video, and Data Files
- HighGUI: Portable Graphics Toolkit
- Working with Image Files
- Loading and Saving Images
- Reading files with cv::imread()
- Writing files with cv::imwrite()
- A Note About Codecs
- Compression and Decompression
- Compressing files with cv::imencode()
- Uncompressing files with cv::imdecode()
- Loading and Saving Images
- Working with Video
- Reading Video with the cv::VideoCapture Object
- Reading frames with cv::VideoCapture::read()
- Reading frames with cv::VideoCapture::operator>>()
- Reading frames with cv::VideoCapture::grab() and cv::VideoCapture::retrieve()
- Camera properties: cv::VideoCapture::get() and cv::VideoCapture::set()
- Writing Video with the cv::VideoWriter Object
- Writing frames with cv::VideoWriter::write()
- Writing frames with cv::VideoWriter::operator<<()
- Reading Video with the cv::VideoCapture Object
- Data Persistence
- Writing to a cv::FileStorage
- Reading from a cv::FileStorage
- cv::FileNode
- Summary
- Exercises
- 9. Cross-Platform and Native Windows
- Working with Windows
- HighGUI Native Graphical User Interface
- Creating a window with cv::namedWindow()
- Drawing an image with cv::imshow()
- Updating a window and cv::waitKey()
- An example displaying an image
- Mouse events
- Sliders, trackbars, and switches
- Surviving without buttons
- Working with the Qt Backend
- Getting started
- The actions menu
- The text overlay
- Writing your own text into the status bar
- The properties window
- Trackbars revisited
- Creating buttons with cv::createButton()
- Text and fonts
- Setting and getting window properties
- Saving and recovering window state
- Interacting with OpenGL
- Integrating OpenCV with Full GUI Toolkits
- An example of OpenCV and Qt
- An example of OpenCV and wxWidgets
- An example of OpenCV and the Windows Template Library
- HighGUI Native Graphical User Interface
- Summary
- Exercises
- Working with Windows
- 10. Filters and Convolution
- Overview
- Before We Begin
- Filters, Kernels, and Convolution
- Anchor points
- Border Extrapolation and Boundary Conditions
- Making borders yourself
- Manual extrapolation
- Filters, Kernels, and Convolution
- Threshold Operations
- Otsus 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
- cv::getDerivKernel()
- cv::getGaussianKernel()
- Summary
- Exercises
- 11. General Image Transforms
- Overview
- Stretch, Shrink, Warp, and Rotate
- Uniform Resize
- cv::resize()
- Image Pyramids
- cv::pyrDown()
- cv::buildPyramid()
- cv::pyrUp()
- The Laplacian pyramid
- Nonuniform Mappings
- Affine Transformation
- cv::warpAffine(): Dense affine transformations
- cv::getAffineTransform(): Computing an affine map matrix
- cv::transform(): Sparse affine transformations
- cv::invertAffineTransform(): Inverting an affine transformation
- Perspective Transformation
- cv::warpPerspective(): Dense perspective transform
- cv::getPerspectiveTransform(): Computing the perspective map matrix
- cv::perspectiveTransform(): Sparse perspective transformations
- Uniform Resize
- General Remappings
- Polar Mappings
- cv::cartToPolar(): Converting from Cartesian to polar coordinates
- cv::polarToCart(): Converting from polar to Cartesian coordinates
- LogPolar
- cv::logPolar()
- Arbitrary Mappings
- cv::remap(): General image remapping
- Polar Mappings
- Image Repair
- Inpainting
- Denoising
- Basic FNLMD with cv::fastNlMeansDenoising()
- FNLMD on color images with cv::fastNlMeansDenoisingColor()
- FNLMD on video with cv::fastNlMeansDenoisingMulti() and cv::fastNlMeansDenoisingColorMulti()
- Histogram Equalization
- cv::equalizeHist(): Contrast equalization
- Summary
- Exercises
- 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
- cv::HoughLines(): The standard and multiscale Hough transforms
- cv::HoughLinesP(): The progressive probabilistic Hough transform
- Hough Circle Transform
- cv::HoughCircles(): the Hough circle transform
- Hough Line 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
- 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
- Correlation method (cv::COMP_CORREL)
- Chi-square method (cv::COMP_CHISQR_ALT)
- Intersection method (cv::COMP_INTERSECT)
- Bhattacharyya distance method (cv::COMP_BHATTACHARYYA)
- Histogram Usage Examples
- Some More Sophisticated Histograms Methods
- Earth Movers Distance
- Back Projection
- Basic back projection: cv::calcBackProject()
- 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
- Histogram Representation in OpenCV
- 14. Contours
- Contour Finding
- Contour Hierarchies
- Finding contours with cv::findContours()
- Drawing Contours
- A Contour Example
- Another Contour Example
- Fast Connected Component Analysis
- Contour Hierarchies
- More to Do with Contours
- Polygon Approximations
- Polygon approximation with cv::approxPolyDP()
- The Douglas-Peucker algorithm explained
- Geometry and Summary Characteristics
- Length using cv::arcLength()
- Upright bounding box with cv::boundingRect()
- A minimum area rectangle with cv::minAreaRect()
- A minimal enclosing circle using cv::minEnclosingCircle()
- Fitting an ellipse with cv::fitEllipse()
- Finding the best line fit to your contour with cv::fitLine()
- Finding the convex hull of a contour using cv::convexHull()
- Geometrical Tests
- Testing if a point is inside a polygon with cv::pointPolygonTest()
- Testing whether a contour is convex with cv::isContourConvex()
- Polygon Approximations
- Matching Contours and Images
- Moments
- Computing moments with cv::moments()
- More About Moments
- Central moments are invariant under translation
- Normalized central moments are also invariant under scaling
- Hu invariant moments are invariant under rotation
- Computing Hu invariant moments with cv::HuMoments()
- Matching and Hu Moments
- Using Shape Context to Compare Shapes
- Structure of the shape module
- The shape context distance extractor
- Hausdorff distance extractor
- Moments
- Summary
- Exercises
- Contour Finding
- 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
- Computing the mean with cv::Mat::operator+=()
- Computing the mean with cv::accumulate()
- Variation: Computing the mean with cv::accumulateWeighted()
- Finding the variance with the help of cv::accumulateSquare()
- Finding the covariance with cv::accumulateWeighted()
- A brief note on model testing and cv::Mahalanobis()
- 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
- cv::BackgroundSubtractorMOG
- Zivkovic Method
- cv::BackgroundSubtractorMOG2
- Summary
- Exercises
- 16. Keypoints and Descriptors
- Keypoints and the Basics of Tracking
- Corner Finding
- Finding corners using cv::goodFeaturesToTrack()
- Subpixel corners
- Introduction to Optical Flow
- Lucas-Kanade Method for Sparse Optical Flow
- How Lucas-Kanade works
- Pyramid Lucas-Kanade code: cv::calcOpticalFlowPyrLK()
- A worked example
- Corner Finding
- Generalized Keypoints and Descriptors
- Optical Flow, Tracking, and Recognition
- How OpenCV Handles Keypoints and Descriptors, the General Case
- The cv::KeyPoint object
- The (abstract) class that finds keypoints and/or computes descriptors for them: cv::Feature2D
- The cv::DMatch object
- The (abstract) keypoint matching class: cv::DescriptorMatcher
- Core Keypoint Detection Methods
- The Harris-Shi-Tomasi feature detector and cv::GoodFeaturesToTrackDetector
- Keypoint finder
- Additional functions
- A brief look under the hood
- The simple blob detector and cv::SimpleBlobDetector
- Keypoint finder
- The FAST feature detector and cv::FastFeatureDetector
- Keypoint finder
- The SIFT feature detector and cv::xfeatures2d::SIFT
- Keypoint finder and feature extractor
- The SURF feature detector and cv::xfeatures2d::SURF
- Keypoint finder and feature extractor
- Additional functions provided by cv::xfeatures2d::SURF
- The Star/CenSurE feature detector and cv::xfeatures2d::StarDetector
- Keypoint finder
- The BRIEF descriptor extractor and cv::BriefDescriptorExtractor
- Feature extractor
- The BRISK algorithm
- Keypoint finder and feature extractor
- Additional functions provided by cv::BRISK
- The ORB feature detector and cv::ORB
- Keypoint finder and feature extractor
- Additional functions provided by cv::ORB
- The FREAK descriptor extractor and cv::xfeatures2d::FREAK
- Feature extractor
- Dense feature grids and the cv::DenseFeatureDetector class
- Keypoint finder
- The Harris-Shi-Tomasi feature detector and cv::GoodFeaturesToTrackDetector
- Keypoint Filtering
- The cv::KeyPointsFilter class
- Matching Methods
- Brute force matching with cv::BFMatcher
- Fast approximate nearest neighbors and cv::FlannBasedMatcher
- Linear indexing with cv::flann::LinearIndexParams
- KD-tree indexing with cv::flann::KDTreeIndexParams
- Hierarchical k-means tree indexing with cv::flann::KMeansIndexParams
- Combining KD-trees and k-means with cv::flann::CompositeIndexParams
- Locality-sensitive hash (LSH) indexing with cv::flann::LshIndexParams
- Automatic index selection with cv::flann::AutotunedIndexParams
- FLANN search parameters and cv::flann::SearchParams
- Displaying Results
- Displaying keypoints with cv::drawKeypoints
- Displaying keypoint matches with cv::drawMatches
- Summary
- Exercises
- Keypoints and the Basics of Tracking
- 17. Tracking
- Concepts in Tracking
- Dense Optical Flow
- The Farnebäck Polynomial Expansion Algorithm
- Computing dense optical flow with cv::calcOpticalFlowFarneback
- The Dual TV-L1 Algorithm
- Computing dense optical flow with cv::createOptFlow_DualTVL1
- The Simple Flow Algorithm
- Computing Simple Flow with cv::optflow::calcOpticalFlowSF()
- The Farnebäck Polynomial Expansion Algorithm
- Mean-Shift and Camshift Tracking
- Mean-Shift
- Camshift
- Motion Templates
- Estimators
- The Kalman Filter
- What goes in and what comes out
- Assumptions required by the Kalman filter
- Information fusion
- Systems with dynamics
- Kalman equations
- Tracking in OpenCV with cv::KalmanFilter
- Kalman filter example code
- A Brief Note on the Extended Kalman Filter
- The Kalman Filter
- Summary
- Exercises
- 18. Camera Models and Calibration
- Camera Model
- The Basics of Projective Geometry
- Rodrigues Transform
- Lens Distortions
- Calibration
- Rotation Matrix and Translation Vector
- Calibration Boards
- Finding chessboard corners with cv::findChessboardCorners()
- Subpixel corners on chessboards and cv::cornerSubPix()
- Drawing chessboard corners with cv::drawChessboardCorners()
- Circle-grids and cv::findCirclesGrid()
- Homography
- Camera Calibration
- How many chess corners for how many parameters?
- Whats under the hood?
- Calibration function
- Computing extrinsics only with cv::solvePnP()
- Computing extrinsics only with cv::solvePnPRansac()
- 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
- Camera Model
- 19. Projection and Three-Dimensional Vision
- Projections
- Affine and Perspective Transformations
- Birds-Eye-View Transform Example
- Three-Dimensional Pose Estimation
- Pose Estimation from a Single Camera
- Computing the pose of a known object with cv::solvePnP()
- Pose Estimation from a Single Camera
- Stereo Imaging
- Triangulation
- Epipolar Geometry
- The Essential and Fundamental Matrices
- Essential matrix math
- Fundamental matrix math
- How OpenCV handles all of this
- Computing Epipolar Lines
- Stereo Calibration
- Stereo Rectification
- Uncalibrated stereo rectification: Hartleys algorithm
- Calibrated stereo rectification: Bouguets algorithm
- Rectification map
- Stereo Correspondence
- The stereo matching classes: cv::StereoBM and cv::StereoSGBM
- Block matching
- Computing stereo depths with cv::StereoBM
- Semi-global block matching
- Computing stereo depths with cv::StereoSGBM
- 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
- 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
- Cross-validation, bootstrapping, ROC curves, and confusion matrices
- Cost of misclassification
- Mismatched feature variance
- Cross-validation, bootstrapping, ROC curves, and confusion matrices
- Legacy Routines in the ML Library
- K-Means
- Problems and solutions
- K-means code
- Mahalanobis Distance
- Using the Mahalanobis distance to condition input data
- Using the Mahalanobis distance for classification
- K-Means
- Summary
- Exercises
- What Is Machine Learning?
- 21. StatModel: The Standard Model for Learning in OpenCV
- Common Routines in the ML Library
- Training and the cv::ml::TrainData Structure
- Constructing cv::ml::TrainData
- Constructing cv::ml::TrainData from stored data
- Secret sauce and cv::ml::TrainDataImpl
- Splitting training data
- Accessing cv::ml::TrainData
- Prediction
- Training and the cv::ml::TrainData Structure
- Machine Learning Algorithms Using cv::StatModel
- Nave/Normal Bayes Classifier
- The nave/normal Bayes classifier and cv::ml::NormalBayesClassifier
- Binary Decision Trees
- Regression impurity
- Classification impurity
- OpenCV implementation
- Decision tree usage
- Decision tree results
- Boosting
- AdaBoost
- Boosting code
- Random Trees
- Random trees code
- Using random trees
- Expectation Maximization
- Expectation maximization with cv::EM()
- K-Nearest Neighbors
- Using K-nearest neighbors with cv::ml::KNearest()
- Multilayer Perceptron
- Back propagation
- The Rprop algorithm
- Using artificial neural networks and back propagation with cv::ml::ANN_MLP
- Parameters for training
- Support Vector Machine
- About kernels
- Handling outliers
- Multiclass extension of SVM
- One-class SVM
- Support vector regression
- Using support vector machines and cv::ml::SVM()
- Additional members of cv::ml::SVM
- Nave/Normal Bayes Classifier
- Summary
- Exercises
- Common Routines in the ML Library
- 22. Object Detection
- Tree-Based Object Detection Techniques
- Cascade Classifiers
- Haar-like features
- Local binary pattern features
- Training and pretrained detectors
- Supervised Learning and Boosting Theory
- Boosting in the Haar cascade
- Rejection cascades
- Viola-Jones classifier summary
- The cv::CascadeClassifer object
- Searching an image with detectMultiScale()
- Face detection example
- Boosting in the Haar cascade
- Learning New Objects
- Detailed arguments to createsamples
- Detailed arguments to traincascade
- Cascade Classifiers
- Object Detection Using Support Vector Machines
- Latent SVM for Object Detection
- Object detection with cv::dpm::DPMDetector
- Other methods of cv::dpm::DPMDetector
- Where to get models for cv::dpm::DPMDetector
- The Bag of Words Algorithm and Semantic Categorization
- Training with cv::BOWTrainer
- K-means and cv::BOWKMeansTrainer
- Categorization with cv::BOWImgDescriptorExtractor
- Putting it together using a support vector machine
- Latent SVM for Object Detection
- Summary
- Exercises
- Tree-Based Object Detection Techniques
- 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
- Past and Present
- A. Planar Subdivisions
- Delaunay Triangulation, Voronoi Tesselation
- Creating a Delaunay or Voronoi Subdivision
- Navigating Delaunay Subdivisions
- Points from edges
- Locating a point within a subdivision
- Orbiting around a vertex
- Rotating an edge
- Identifying the bounding triangle
- Identifying the bounding triangle or edges on the convex hull and walking the hull
- Usage Examples
- Exercises
- Delaunay Triangulation, Voronoi Tesselation
- B. opencv_contrib
- An Overview of the opencv_contrib Modules
- Contents of opencv_contrib
- An Overview of the opencv_contrib Modules
- C. Calibration Patterns
- Calibration Patterns Used by OpenCV
- Bibliography
- Index
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