Key Features
- This book provides hands-on examples that cover the major features that are part of any important Computer Vision application
- It explores important algorithms that allow you to recognize faces, identify objects, track camera movements, and much more
- We share best practices and tips so you appreciate the power of OpenCV
Book Description
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 involved in building and deploying an end-to-end application in the domain of computer vision using OpenCV/C++.
It starts with instructions on how to install the library and ends with you having developed an application that does something tangible and useful in computer vision/machine learning.
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 so on), 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 will 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!
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 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
- Get 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
Samyak Datta has a Bachelor's and a Master's degree in Computer Science from the Indian Institute of Technology, Roorkee. He is a computer vision and machine learning enthusiast with over 3 years of experience. His first contact with OpenCV was in 2013 when he was working on his Master's' thesis, and since then there has been no looking back. He has also been a contributor to OpenCV's GitHub repository.
Over the course of his undergraduate and Master's degree, Samyak has had the opportunity to engage with both the industry and research. He worked with Google India and Media.net (Directi) as a software engineering intern, where he was involved with projects ranging from machine learning and natural language processing to computer vision. As of 2016, he is a research intern at the Center for Visual Information Technology (CVIT) at the Indian Institute of Information Technology - Hyderabad.
He undertook a project on "Machine Learning - Gender Classification from Facial Images" in April 2015.
