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.
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Mastering OpenCV with Practical Computer Vision Projects

Daniel Lélis Baggio et al.

2 customer reviews
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.
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Book Details

ISBN 139781849517829
Paperback340 pages

Book Description

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 OpenCV’s 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 you’re 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.

Table of Contents

Chapter 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
Porting from desktop to Android
Summary
Chapter 2: Marker-based Augmented Reality on iPhone or iPad
Creating an iOS project that uses OpenCV
Application architecture
Marker detection
Placing a marker in 3D
Rendering the 3D virtual object
Summary
References
Chapter 3: Marker-less Augmented Reality
Marker-based versus marker-less AR
Using feature descriptors to find an arbitrary image on video
Pattern pose estimation
Application infrastructure
Summary
References
Chapter 4: Exploring Structure from Motion Using OpenCV
Structure from Motion concepts
Estimating the camera motion from a pair of images
Reconstructing the scene
Reconstruction from many views
Refinement of the reconstruction
Visualizing 3D point clouds with PCL
Using the example code
Summary
References
Chapter 5: Number Plate Recognition Using SVM and Neural Networks
Introduction to ANPR
ANPR algorithm
Plate detection
Plate recognition
Summary
Chapter 6: Non-rigid Face Tracking
Overview
Utilities
Geometrical constraints
Facial feature detectors
Face detection and initialization
Face tracking
Summary
References
Chapter 7: 3D Head Pose Estimation Using AAM and POSIT
Active Appearance Models overview
Active Shape Models
Model Instantiation – playing with the Active Appearance Model
AAM search and fitting
POSIT
Summary
References
Chapter 8: Face Recognition using Eigenfaces or Fisherfaces
Introduction to face recognition and face detection
Summary
References

What You Will Learn

  • Perform Face Analysis including simple Face & Eye & Skin Detection, Fisherfaces Face Recognition, 3D Head Orientation, complex Facial Feature Tracking.
  • Do Number Plate Detection and Optical Character Recognition (OCR) using Artificial Intelligence (AI) methods including SVMs and Neural Networks
  • Learn Augmented Reality for desktop and iPhone or iPad using simple artificial markers or complex markerless natural images
  • Generate a 3D object model by moving a plain 2D camera, using 3D Structure from Motion (SfM) camera reprojection methods
  • Redesign desktop real-time computer vision applications to more suitable Android & iOS mobile apps
  • Use simple image filter effects including cartoon, sketch, paint, and alien effects
  • Execute Human-Computer Interaction with an XBox Kinect sensor using the whole body as a dynamic input

Authors

Table of Contents

Chapter 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
Porting from desktop to Android
Summary
Chapter 2: Marker-based Augmented Reality on iPhone or iPad
Creating an iOS project that uses OpenCV
Application architecture
Marker detection
Placing a marker in 3D
Rendering the 3D virtual object
Summary
References
Chapter 3: Marker-less Augmented Reality
Marker-based versus marker-less AR
Using feature descriptors to find an arbitrary image on video
Pattern pose estimation
Application infrastructure
Summary
References
Chapter 4: Exploring Structure from Motion Using OpenCV
Structure from Motion concepts
Estimating the camera motion from a pair of images
Reconstructing the scene
Reconstruction from many views
Refinement of the reconstruction
Visualizing 3D point clouds with PCL
Using the example code
Summary
References
Chapter 5: Number Plate Recognition Using SVM and Neural Networks
Introduction to ANPR
ANPR algorithm
Plate detection
Plate recognition
Summary
Chapter 6: Non-rigid Face Tracking
Overview
Utilities
Geometrical constraints
Facial feature detectors
Face detection and initialization
Face tracking
Summary
References
Chapter 7: 3D Head Pose Estimation Using AAM and POSIT
Active Appearance Models overview
Active Shape Models
Model Instantiation – playing with the Active Appearance Model
AAM search and fitting
POSIT
Summary
References
Chapter 8: Face Recognition using Eigenfaces or Fisherfaces
Introduction to face recognition and face detection
Summary
References

Book Details

ISBN 139781849517829
Paperback340 pages
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From 2 reviews

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