Mastering OpenCV with Practical Computer Vision Projects. This is the definitive advanced tutorial for OpenCV, designed for those with basic C++ skills. The computer vision projects are divided into easily assimilated chapters with an emphasis on practical involvement for an easier learning curve Shervin Emami

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Spis treści książki

• Mastering OpenCV with Practical Computer Vision Projects
  • Table of Contents
  • Mastering OpenCV with Practical Computer Vision Projects
  • Credits
  • About the Authors
  • About the Reviewers
  • www.PacktPub.com
    • Support files, eBooks, discount offers and more
      • Why Subscribe?
      • Free Access for Packt account holders
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Errata
      • Piracy
      • Questions
  • 1. Cartoonifier and Skin Changer for Android
    • Accessing the webcam
    • Main camera processing loop for a desktop app
    • Generating a black-and-white sketch
    • Generating a color painting and a cartoon
    • Generating an "evil" mode using edge filters
    • Generating an "alien" mode using skin detection
      • Skin-detection algorithm
      • Showing the user where to put their face
      • Implementation of the skin-color changer
    • Porting from desktop to Android
      • Setting up an Android project that uses OpenCV
        • Color formats used for image processing on Android
        • Input color format from the camera
        • Output color format for display
      • Adding the cartoonifier code to the Android NDK app
        • Reviewing the Android app
        • Cartoonifying the image when the user taps the screen
        • Saving the image to a file and to the Android picture gallery
      • Showing an Android notification message about a saved image
        • Changing cartoon modes through the Android menu bar
      • Reducing the random pepper noise from the sketch image
        • Showing the FPS of the app
        • Using a different camera resolution
        • Customizing the app
    • Summary
  • 2. Marker-based Augmented Reality on iPhone or iPad
    • Creating an iOS project that uses OpenCV
      • Adding OpenCV framework
      • Including OpenCV headers
    • Application architecture
      • Accessing the camera
    • Marker detection
      • Marker identification
        • Grayscale conversion
        • Image binarization
        • Contours detection
        • Candidates search
      • Marker code recognition
        • Reading marker code
        • Marker location refinement
    • Placing a marker in 3D
      • Camera calibration
      • Marker pose estimation
    • Rendering the 3D virtual object
      • Creating the OpenGL rendering layer
      • Rendering an AR scene
    • Summary
    • References
  • 3. Marker-less Augmented Reality
    • Marker-based versus marker-less AR
    • Using feature descriptors to find an arbitrary image on video
      • Feature extraction
      • Definition of a pattern object
      • Matching of feature points
        • PatternDetector.cpp
      • Outlier removal
        • Cross-match filter
        • Ratio test
          • PatternDetector.cpp
        • Homography estimation
          • PatternDetector.cpp
        • Homography refinement
          • PatternDetector.cpp
      • Putting it all together
    • Pattern pose estimation
      • PatternDetector.cpp
      • Obtaining the camera-intrinsic matrix
        • Pattern.cpp
    • Application infrastructure
      • ARPipeline.hpp
      • ARPipeline.cpp
      • Enabling support for 3D visualization in OpenCV
      • Creating OpenGL windows using OpenCV
      • Video capture using OpenCV
      • Rendering augmented reality
        • ARDrawingContext.hpp
        • ARDrawingContext.cpp
      • Demonstration
        • main.cpp
    • Summary
    • References
  • 4. Exploring Structure from Motion Using OpenCV
    • Structure from Motion concepts
    • Estimating the camera motion from a pair of images
      • Point matching using rich feature descriptors
      • Point matching using optical flow
      • Finding camera matrices
    • Reconstructing the scene
    • Reconstruction from many views
    • Refinement of the reconstruction
    • Visualizing 3D point clouds with PCL
    • Using the example code
    • Summary
    • References
  • 5. Number Plate Recognition Using SVM and Neural Networks
    • Introduction to ANPR
    • ANPR algorithm
    • Plate detection
      • Segmentation
      • Classification
    • Plate recognition
      • OCR segmentation
      • Feature extraction
      • OCR classification
      • Evaluation
    • Summary
  • 6. Non-rigid Face Tracking
    • Overview
    • Utilities
      • Object-oriented design
      • Data collection: Image and video annotation
        • Training data types
        • Annotation tool
        • Pre-annotated data (The MUCT dataset)
    • Geometrical constraints
      • Procrustes analysis
      • Linear shape models
      • A combined local-global representation
      • Training and visualization
    • Facial feature detectors
      • Correlation-based patch models
        • Learning discriminative patch models
        • Generative versus discriminative patch models
      • Accounting for global geometric transformations
      • Training and visualization
    • Face detection and initialization
    • Face tracking
      • Face tracker implementation
      • Training and visualization
      • Generic versus person-specific models
    • Summary
    • References
  • 7. 3D Head Pose Estimation Using AAM and POSIT
    • Active Appearance Models overview
    • Active Shape Models
      • Getting the feel of PCA
      • Triangulation
      • Triangle texture warping
    • Model Instantiation playing with the Active Appearance Model
    • AAM search and fitting
    • POSIT
      • Diving into POSIT
      • POSIT and head model
      • Tracking from webcam or video file
    • Summary
    • References
  • 8. Face Recognition using Eigenfaces or Fisherfaces
    • Introduction to face recognition and face detection
      • Step 1: Face detection
        • Implementing face detection using OpenCV
        • Loading a Haar or LBP detector for object or face detection
        • Accessing the webcam
        • Detecting an object using the Haar or LBP Classifier
          • Grayscale color conversion
          • Shrinking the camera image
          • Histogram equalization
      • Detecting the face
      • Step 2: Face preprocessing
        • Eye detection
        • Eye search regions
          • Geometrical transformation
          • Separate histogram equalization for left and right sides
          • Smoothing
          • Elliptical mask
      • Step 3: Collecting faces and learning from them
        • Collecting preprocessed faces for training
        • Training the face recognition system from collected faces
        • Viewing the learned knowledge
        • Average face
        • Eigenvalues, Eigenfaces, and Fisherfaces
      • Step 4: Face recognition
        • Face identification: Recognizing people from their face
        • Face verification: Validating that it is the claimed person
      • Finishing touches: Saving and loading files
      • Finishing touches: Making a nice and interactive GUI
        • Drawing the GUI elements
          • Startup mode
          • Detection mode
          • Collection mode
          • Training mode
          • Recognition mode
        • Checking and handling mouse clicks
    • Summary
    • References
  • Index

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