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LEARNING OPENCV 3. COMPUTER VISION IN C++ WITH THE OPENCV LIBRARY
Título:
LEARNING OPENCV 3. COMPUTER VISION IN C++ WITH THE OPENCV LIBRARY
Subtítulo:
Autor:
KAEHLER, A
Editorial:
O´REILLY
Año de edición:
2016
Materia
VISION POR ORDENADOR
ISBN:
978-1-4919-3799-0
Páginas:
1024
69,95 €

 

Sinopsis

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



Chapter 1Overview
What Is OpenCV?
Who Uses OpenCV?
What Is Computer Vision?
The Origin of OpenCV
Downloading and Installing OpenCV
Getting the Latest OpenCV via Git
More OpenCV Documentation
OpenCV Contribution Repository
Portability
Summary
Exercises
Chapter 2Introduction to OpenCV
Include Files
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 3Getting to Know OpenCV Data Types
The Basics
OpenCV Data Types
Summary
Exercises
Chapter 4Images and Large Array Types
Dynamic and Variable Storage
Summary
Exercises
Chapter 5Array Operations
More Things You Can Do with Arrays
Summary
Exercises
Chapter 6Drawing and Annotating
Drawing Things
Summary
Exercises
Chapter 7Functors in OpenCV
Objects That "Do Stuffö
Summary
Exercises
Chapter 8Image, Video, and Data Files
HighGUI: Portable Graphics Toolkit
Working with Image Files
Working with Video
Data Persistence
Summary
Exercises
Chapter 9Cross-Platform and Native Windows
Working with Windows
Summary
Exercises
Chapter 10Filters and Convolution
Overview
Before We Begin
Threshold Operations
Smoothing
Derivatives and Gradients
Image Morphology
Convolution with an Arbitrary Linear Filter
Summary
Exercises
Chapter 11General Image Transforms
Overview
Stretch, Shrink, Warp, and Rotate
General Remappings
Image Repair
Histogram Equalization
Summary
Exercises
Chapter 12Image Analysis
Overview
Discrete Fourier Transform
Integral Images
The Canny Edge Detector
Hough Transforms
Distance Transformation
Segmentation
Summary
Exercises
Chapter 13Histograms and Templates
Histogram Representation in OpenCV
Basic Manipulations with Histograms
Some More Sophisticated Histograms Methods
Template Matching
Summary
Exercises
Chapter 14Contours
Contour Finding
More to Do with Contours
Matching Contours and Images
Summary
Exercises
Chapter 15Background Subtraction
Overview of Background Subtraction
Weaknesses of Background Subtraction
Scene Modeling
Averaging Background Method
A More Advanced Background Subtraction Method
Connected Components for Foreground Cleanup
Comparing Two Background Methods
OpenCV Background Subtraction Encapsulation
Summary
Exercises
Chapter 16Keypoints and Descriptors
Keypoints and the Basics of Tracking
Generalized Keypoints and Descriptors
Summary
Exercises
Chapter 17Tracking
Concepts in Tracking
Dense Optical Flow
Mean-Shift and Camshift Tracking
Motion Templates
Estimators
Summary
Exercises
Chapter 18Camera Models and Calibration
Camera Model
Calibration
Undistortion
Putting Calibration All Together
Summary
Exercises
Chapter 19Projection and Three-Dimensional Vision
Projections
Affine and Perspective Transformations
Three-Dimensional Pose Estimation
Stereo Imaging
Structure from Motion
Fitting Lines in Two and Three Dimensions
Summary
Exercises
Chapter 20The Basics of Machine Learning in OpenCV
What Is Machine Learning?
Legacy Routines in the ML Library
Summary
Exercises
Chapter 21StatModel: The Standard Model for Learning in OpenCV
Common Routines in the ML Library
Machine Learning Algorithms Using cv::StatModel
Summary
Exercises
Chapter 22Object Detection
Tree-Based Object Detection Techniques
Object Detection Using Support Vector Machines
Summary
Exercises
Chapter 23Future of OpenCV
Past and Present
How Well Did Our Predictions Go Last Time?
Future Functions
Some AI Speculation
Afterword
Appendix Planar Subdivisions
Delaunay Triangulation, Voronoi Tesselation
Exercises
Appendix opencv_contrib
An Overview of the opencv_contrib Modules
Appendix Calibration Patterns
Calibration Patterns Used by OpenCV