- 374 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
OpenCV Computer Vision Application Programming Cookbook Second Edition
About this book
OpenCV Computer Vision Application Programming Cookbook Second Edition is your guide to the development of computer vision applications.
The book shows you how to install and deploy the OpenCV library to write an effective computer vision application. Different techniques for image enhancement, pixel manipulation, and shape analysis will be presented. You will also learn how to process video from files or cameras and detect and track moving objects. You will also be introduced to recent approaches in machine learning and object classification.
This book is a comprehensive reference guide that exposes you to practical and fundamental computer vision concepts, illustrated by extensive examples.
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Yes, you can access OpenCV Computer Vision Application Programming Cookbook Second Edition by Robert Laganiere in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Vision & Pattern Recognition. We have over one million books available in our catalogue for you to explore.
Information
OpenCV Computer Vision Application Programming Cookbook Second Edition
Table of Contents
OpenCV Computer Vision Application Programming Cookbook Second Edition
Credits
About the Author
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. Playing with Images
Introduction
Installing the OpenCV library
Getting ready
How to do it...
How it works...
There's more...
Using Qt for OpenCV developments
The OpenCV developer site
See also
Loading, displaying, and saving images
Getting ready
How to do it...
How it works...
There's more...
Clicking on images
Drawing on images
Running the example with Qt
See also
Exploring the cv::Mat data structure
How to do it...
How it works...
There's more...
The input and output arrays
The old IplImage structure
See also
Defining regions of interest
Getting ready
How to do it...
How it works...
There's more...
Using image masks
See also
2. Manipulating Pixels
Introduction
Accessing pixel values
Getting ready
How to do it...
How it works...
There's more...
The cv::Mat_ template class
See also
Scanning an image with pointers
Getting ready
How to do it...
How it works...
There's more...
Other color reduction formulas
Having input and output arguments
Efficient scanning of continuous images
Low-level pointer arithmetics
See also
Scanning an image with iterators
Getting ready
How to do it...
How it works...
There's more...
See also
Writing efficient image-scanning loops
How to do it...
How it works...
There's more…
See also
Scanning an image with neighbor access
Getting ready
How to do it...
How it works...
There's more...
See also
Performing simple image arithmetic
Getting ready
How to do it...
How it works...
There's more...
Overloaded image operators
Splitting the image channels
Remapping an image
How to do it...
How it works...
See also
3. Processing Color Images with Classes
Introduction
Using the Strategy pattern in an algorithm design
Getting ready
How to do it…
How it works…
There's more…
Computing the distance between two color vectors
Using OpenCV functions
The functor or function object
See also
Using a Controller design pattern to communicate with processing modules
Getting ready
How to do it…
How it works…
There's more…
The Model-View-Controller architecture
Converting color representations
Getting ready
How to do it…
How it works…
See also
Representing colors with hue, saturation, and brightness
How to do it…
How it works…
There's more…
Using colors for detection – skin tone detection
4. Counting the Pixels with Histograms
Introduction
Computing the image histogram
Getting started
How to do it...
How it works...
There's more...
Computing histograms of color images
See also
Applying look-up tables to modify the image appearance
How to do it...
How it works...
There's more...
Stretching a histogram to improve the image contrast
Applying a look-up table on color images
See also
Equalizing the image histogram
How to do it...
How it works...
Backprojecting a histogram to detect specific image content
How to do it...
How it works...
There's more...
Backprojecting color histograms
See also
Using the mean shift algorithm to find an object
How to do it...
How it works...
See also
Retrieving similar images using the histogram comparison
How to do it...
How it works...
See also
Counting pixels with integral images
How to do it...
How it works...
There's more...
Adaptive thresholding
Visual tracking using histograms
See also
5. Transforming Images with Morphological Operations
Introduction
Eroding and dilating images using morphological filters
Getting ready
How to do it...
How it works...
There's more...
See also
Opening and closing images using morphological filters
How to do it...
How it works...
See also
Detecting edges and corners using morphological filters
Getting ready
How to do it...
How it works...
See also
Segmenting images using watersheds
How to do it...
How it works...
There's more...
See also
Extracting distinctive regions using MSER
How to do it...
How it works...
See also
Extracting foreground objects with the GrabCut algorithm
How to do it...
How it works...
See also
6. Filtering the Images
Introduction
Filtering images using low-pass filters
How to do it...
How it works...
There's more...
Downsampling an image
Interpolating pixel values
See also
Filtering images using a median filter
How to do it...
How it works...
Applying directional filters to detect edges
How to do it...
How it works...
There's more...
Gradient operators
Gaussian derivatives
See also
Computing the Laplacian of an image
How to do it...
How it works...
There's more...
Enhancing the contrast of an image using the Laplacian
Difference of Gaussians
See also
7. Extracting Lines, Contours, and Components
Introduction
Detecting image contours with the Canny operator
How to do it...
How it works...
See also
Detecting lines in images with the Hough transform
Getting ready
How to do it...
How it works...
There's more...
Detecting circles
See also
Fitting a line to a set of points
How to do it...
How it works...
There's more...
Extracting the components' contours
How to do it...
How it works...
There's more...
Computing components' shape descriptors
How to do it...
How it works...
There's more...
Quadrilateral detection
8. Detecting Interest Points
Introduction
Detecting corners in an image
How to do it...
How it works...
There's more...
Good features to track
The feature detector's common interface
See also
Detecting features quickly
How to do it...
How it works...
There's more...
Adapted feature detection
Grid adapted feature detection
Pyramid adapted feature detection
See also
Detecting scale-invariant features
How to do it...
How it works...
There's more...
The SIFT feature-detection algorithm
See also
Detecting FAST features at multiple scales
How to do it...
How it works...
There's more...
The ORB feature-detection algorithm
See also
9. Describing and Matching Interest Points
Introduction
Matching local templates
How to do it...
How it works...
There's more...
Template matching
See also
Describing local intensity patterns
How to do it...
How it works...
There's more...
Cross-checking matches
The ratio test
Distance thresholding
See also
Describing keypoints with binary features
How to do it...
How it works...
There's more...
FREAK
See also
10. Estimating Projective Relations in Image...
Table of contents
- OpenCV Computer Vision Application Programming Cookbook Second Edition