Are you looking forward to developing interesting computer vision applications? If yes, then this Learning Path is for you. Computer vision and machine learning concepts are frequently used in practical projects based on computer vision. Whether you are completely new to the concept of computer vision or have a basic understanding of it, this Learning Path will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects.

OpenCV is a cross-platform, open source library that is used for face recognition, object tracking, and image and video processing. Learning the basic concepts of computer vision algorithms, models, and OpenCV’s API will help you develop all sorts of real-world applications.

Starting from the installation of OpenCV 3 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. You’ll explore the commonly-used computer vision techniques to build your own OpenCV projects from scratch. Next, we’ll teach you how to work with the various OpenCV modules for statistical modeling and machine learning. You’ll start by preparing your data for analysis, learn about supervised and unsupervised learning, and see how to use them. Finally, you’ll learn to implement efficient models using the popular machine learning techniques such as classification, regression, decision trees, K-nearest neighbors, boosting, and neural networks with the aid of C++ and OpenCV.

By the end of this Learning Path, you will be familiar with the basics of OpenCV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.