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  • OpenCV Computer Vision Application Programming Cookbook Second Edition
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OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition

By : Robert Laganiere
3.7 (3)
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OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition

3.7 (3)
By: Robert Laganiere

Overview of this book

OpenCV 3 Computer Vision Application Programming Cookbook is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.
Table of Contents (13 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Playing with Images
1. Playing with Images
Introduction
Installing the OpenCV library
Loading, displaying, and saving images
Exploring the cv::Mat data structure
Defining regions of interest
2
2. Manipulating Pixels
2. 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
3. Processing Color Images with Classes
3. 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
4. Counting the Pixels with Histograms
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4. 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
5. Transforming Images with Morphological Operations
5. 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
6. Filtering the Images
6. 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
7. Extracting Lines, Contours, and Components
7. 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
8. Detecting Interest Points
8. Detecting Interest Points
Introduction
Detecting corners in an image
Detecting features quickly
Detecting scale-invariant features
Detecting FAST features at multiple scales
9
9. Describing and Matching Interest Points
9. Describing and Matching Interest Points
Introduction
Matching local templates
Describing local intensity patterns
Describing keypoints with binary features
10
10. Estimating Projective Relations in Images
10. 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
11. Processing Video Sequences
11. 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
12
Index
Index

Applying look-up tables to modify the image appearance

Image histograms capture the way a scene is rendered using the available pixel intensity values. By analyzing the distribution of the pixel values over an image, it is possible to use this information to modify and possibly improve an image. This recipe explains how we can use a simple mapping function, represented by a look-up table, to modify the pixel values of an image. As we will see, look-up tables are often defined from histogram distributions.

How to do it...

A look-up table is a simple one-to-one (or many-to-one) function that defines how pixel values are transformed into new values. It is a 1D array with, in the case of regular gray-level images, 256 entries. Entry i of the table gives you the new intensity value of the corresponding gray level, which is as follows:

         newIntensity= lookup[oldIntensity];

The cv::LUT function in OpenCV applies a look-up table to an image in order to produce a new image. We can add this function...

End of Section 7
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