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OpenCV Computer Vision Application Programming Cookbook Second Edition

By : Robert Laganiere
Book Image

OpenCV Computer Vision Application Programming Cookbook Second Edition

By: Robert Laganiere

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OpenCV Computer Vision Application Programming Cookbook Second Edition
Robert Laganiere
Aug, 2014 | 374 pages
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Table of Contents (18 chapters)
OpenCV Computer Vision Application Programming Cookbook Second Edition
OpenCV Computer Vision Application Programming Cookbook Second Edition
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Playing with Images
Playing with Images
Introduction
Installing the OpenCV library
Loading, displaying, and saving images
Exploring the cv::Mat data structure
Defining regions of interest
2
Manipulating Pixels
Manipulating Pixels
Introduction
Accessing pixel values
Scanning an image with pointers
Scanning an image with iterators
Writing efficient image-scanning loops
Scanning an image with neighbor access
Performing simple image arithmetic
Remapping an image
3
Processing Color Images with Classes
Processing Color Images with Classes
Introduction
Using the Strategy pattern in an algorithm design
Using a Controller design pattern to communicate with processing modules
Converting color representations
Representing colors with hue, saturation, and brightness
4
Counting the Pixels with Histograms
Counting the Pixels with Histograms
Introduction
Computing the image histogram
Applying look-up tables to modify the image appearance
Equalizing the image histogram
Backprojecting a histogram to detect specific image content
Using the mean shift algorithm to find an object
Retrieving similar images using the histogram comparison
Counting pixels with integral images
5
Transforming Images with Morphological Operations
Transforming Images with Morphological Operations
Introduction
Eroding and dilating images using morphological filters
Opening and closing images using morphological filters
Detecting edges and corners using morphological filters
Segmenting images using watersheds
Extracting distinctive regions using MSER
Extracting foreground objects with the GrabCut algorithm
6
Filtering the Images
Filtering the Images
Introduction
Filtering images using low-pass filters
Filtering images using a median filter
Applying directional filters to detect edges
Computing the Laplacian of an image
7
Extracting Lines, Contours, and Components
Extracting Lines, Contours, and Components
Introduction
Detecting image contours with the Canny operator
Detecting lines in images with the Hough transform
Fitting a line to a set of points
Extracting the components' contours
Computing components' shape descriptors
8
Detecting Interest Points
Detecting Interest Points
Introduction
Detecting corners in an image
Detecting features quickly
Detecting scale-invariant features
Detecting FAST features at multiple scales
9
Describing and Matching Interest Points
Describing and Matching Interest Points
Introduction
Matching local templates
Describing local intensity patterns
Describing keypoints with binary features
10
Estimating Projective Relations in Images
Estimating Projective Relations in Images
Introduction
Calibrating a camera
Computing the fundamental matrix of an image pair
Matching images using a random sample consensus
Computing a homography between two images
11
Processing Video Sequences
Processing Video Sequences
Introduction
Reading video sequences
Processing the video frames
Writing video sequences
Tracking feature points in a video
Extracting the foreground objects in a video
Index
Index

Chapter 10. Estimating Projective Relations in Images

In this chapter, we will cover the following recipes:

  • Calibrating a camera

  • Computing the fundamental matrix of an image pair

  • Matching images using a random sample consensus

  • Computing a homography between two images

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