Learning OpenCV 3. Computer Vision in C++ with the OpenCV Library Adrian Kaehler, Gary Bradski

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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...
• 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
  • 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
• 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
• 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
• 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()
  • Summary
  • Exercises
• 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()
  • Summary
  • Exercises
• 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()
  • 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<<()
  • 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
  • Summary
  • Exercises
• 10. Filters and Convolution
  • Overview
  • Before We Begin
    • Filters, Kernels, and Convolution
      • Anchor points
    • Border Extrapolation and Boundary Conditions
      • Making borders yourself
      • Manual extrapolation
  • 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
  • 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
  • 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
  • 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
• 14. Contours
  • Contour Finding
    • Contour Hierarchies
      • Finding contours with cv::findContours()
    • Drawing Contours
    • A Contour Example
    • Another Contour Example
    • Fast Connected Component Analysis
  • 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()
  • 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
  • Summary
  • Exercises
• 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()
  • 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
  • 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
    • 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
• 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()
  • 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
  • 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
• 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()
  • 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
  • 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
  • Summary
  • Exercises
• 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
  • 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
  • Summary
  • Exercises
• 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
    • Learning New Objects
      • Detailed arguments to createsamples
      • Detailed arguments to traincascade
  • 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
  • Summary
  • Exercises
• 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
• 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
• B. opencv_contrib
  • An Overview of the opencv_contrib Modules
    • Contents of opencv_contrib
• C. Calibration Patterns
  • Calibration Patterns Used by OpenCV
• Bibliography
• Index

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