Book Image

Learning OpenCV 3 Application Development

By : Samyak Datta
Book Image

Learning OpenCV 3 Application Development

By: Samyak Datta

Overview of this book

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!
Table of Contents (16 chapters)
Learning OpenCV 3 Application Development
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Types of simple image thresholding


The following table describes the different types of simple image thresholding operations that have been made available by the OpenCV developers:

Threshold Type

Threshold Function

THRESH_BINARY

THRESH_BINARY_INV

THRESH_TRUNC

THRESH_TOZERO

THRESH_TOZERO_INV

THRESH_OTSU

Uses the Otsu's method to compute the optimal threshold value

The flag representing the last thresholding method-Otsu's method is a little different (slightly more complicated) than the others. It relies on Image Histograms, our topic of discussion for the next chapter. For those of you who know what histograms are, the Otsu's method essentially assumes that the histogram for the image consists of two peaks-one corresponding to the background (black) and the other for the foreground (white). The computational steps in the algorithm try to come up with a threshold value that best separates the two peaks in the image histogram. We won't be discussing...