Book Image

OpenCV By Example

By : Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça
Book Image

OpenCV By Example

By: Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça

Overview of this book

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.
Table of Contents (18 chapters)
OpenCV By Example
About the Authors
About the Reviewers

Shi-Tomasi Corner Detector

The Harris corner detector performs well in many cases, but it can still be improved. Around six years after the original paper by Harris and Stephens, Shi-Tomasi came up with something better and they called it Good Features To Track. You can read the original paper at: They used a different scoring function to improve the overall quality. Using this method, we can find the N strongest corners in the given image. This is very useful when we don't want to use every single corner to extract information from the image. As discussed earlier, a good interest point detector is very useful in applications, such as object tracking, object recognition, image search, and so on.

If you apply the Shi-Tomasi corner detector to an image, you will see something like this:

As you can see here, all the important points in the frame are captured. Let's take a look at the following code to track these features:

int main(int...