Yes, computers can see too. Want to know how? This course will not just show you how but equip you with skills to implement your own ideas. Let’s get started!
This course is a blend of text, videos, code examples, and assessments, which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of building cool computer vision applications with OpenCV.
OpenCV is a cross-platform, free-to-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.
This course has been prepared using extensive research and curation skills. Each section adds to the skills learned and helps us to achieve mastery in developing computer vision applications using OpenCV. Every section is modular and can be used as a standalone resource too. This course has been designed to teach you OpenCV through the use of projects and learning recipes, and equip you with skills to develop your own cool applications. This course will take you through the commonly used Computer Vision techniques to build your own OpenCV projects from scratch.
Starting with the installation of OpenCV on your system and understanding the basics of image processing, we will 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 OpenCV projects from scratch. We will develop awesome projects that will focus on the different concepts of computer vision such as image processing, motion detection, and image segmentation. By the end of this course, 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.
This course has been authored by some of the best in their fields:
Prateek Joshi is a Computer Vision researcher and published author. He has over eight years of experience in this field with a primary focus on content-based analysis and deep learning. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences. You can visit his blog.
David Millán Escrivá
David Millán Escrivá has more than 13 years of experience in IT, with more than nine years of experience in Computer Vision, computer graphics, and pattern recognition, working on different projects and start-ups, applying his knowledge of Computer Vision, optical character recognition, and augmented reality. He is the author of the DamilesBlog, where he publishes research articles and tutorials on OpenCV, Computer Vision in general, and optical character recognition algorithms.
Vinícius Godoy is a computer graphics university professor at PUCPR. He started programming with C++ 18 years ago and ventured into the field of computer gaming and computer graphics 10 years ago. He is currently working with medical imaging systems for his PhD thesis.
Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content-based video analysis, visual surveillance, object recognition, and 3D reconstruction. Since 2011, Robert has also been Chief Scientist at Cognivue Corp, a leader in embedded vision solutions.