Author: Samyak Datta
Pub Date: 2017
Size: 34 Mb
Build, create, and deploy your own computer vision applications with the power of OpenCV
Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++.
At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations.
Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code!
The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!
What You Will Learn
- Explore the steps involved in building a typical computer vision/machine learning application
- Understand the relevance of OpenCV at every stage of building an application
- Harness the vast amount of information that lies hidden in images into the apps you build
- Incorporate visual information in your apps to create more appealing software
- Get acquainted with how large-scale and popular image editing apps such as Instagram work behind the scenes by getting a glimpse of how the image filters in apps can be recreated using simple operations in OpenCV
- Appreciate how difficult it is for a computer program to perform tasks that are trivial for human beings
- Get to know how to develop applications that perform face detection, gender detection from facial images, and handwritten character (digit) recognition
Table of Contents
1: Laying the Foundation
2: Image Filtering
3: Image Thresholding
4: Image Histograms
5: Image Derivatives and Edge Detection
6: Face Detection Using OpenCV
7: Affine Transformations and Face Alignment
8: Feature Descriptors in OpenCV
9: Machine Learning with OpenCV
Appendix: Command-line Arguments in C++