- Learn about the computer vision workflows and understand the basic image matrix format and filters
- Understand the segmentation and feature extraction techniques
- Learn how to remove backgrounds from a static scene to identify moving objects for video surveillance
- Use the new OpenCV functions for text detection and recognition with Tesseract
- Master logistic regression and regularization techniques
- Solve image segmentation problem using k-means clustering
- Load models trained with popular deep learning libraries such as Caffe
Are you looking forward to developing interesting computer vision applications? If yes^ then this Learning Path is for you.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
Computer vision and machine learning concepts are frequently used together 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. By learning the basic concepts of computer vision algorithms^ models^ and OpenCV’s API^ you will be able to develop different types of real-world applications.
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 and 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.
Meet Your Experts:
We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:
David Millán Escrivá was eight years old when he wrote his first program on an 8086 PC with Basic language^ which enabled the 2D plotting of basic equations. In 2005^ he finished his studies in IT through the Universitat Politécnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96).
Prateek Joshi is an artificial intelligence researcher^ published author of five books^ and TEDx speaker. He is the founder of Pluto AI^ a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning.
Joe Minichino is a computer vision engineer for Hoolux Medical by day and a developer of the NoSQL database LokiJS by night. At Hoolux^ he leads the development of an Android computer vision-based advertising platform for the medical industry.