OpenCV 3 is a native cross-platform C++ Library for computer vision, machine learning, and image processing. Computer vision applications are the latest buzz of recent time! Big brands such as Microsoft, Apple, Google, Facebook, and Apple are increasingly making use of computer vision for object, pattern, image, and face recognition. This has led to a very high demand for computer vision expertise. So, if you’re interested to know how to use the OpenCV library to build computer vision applications, then go for this Learning Path. Packts 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. The highlights of this Learning Path are: Dive into the essentials of OpenCV and build your own projects Learn how to apply complex visual effects to images Reconstruct a 3D scene from images Master the fundamental concepts in computer vision and image processing Lets take a quick look at your learning journey. This Learning Path helps you to get started with the OpenCV library and shows you how to install and deploy it to write effective computer vision applications following good programming practices. You will learn how to read and display images. You will then be introduced to the basic OpenCV data structures. Further, you will start a new project and see how to load an image file and show it. Next, you’ll find out how to handle keyboard events in our display window. In the next project, you will jump into interactively adjusting image brightness. You will then learn to add a miniaturizing tilt-shift effect and how to blur images. In the final project, you will learn to apply Instagram-like color ambiance filters to images. By the end of this Learning Path, you will be able to build computer vision applications that make the most of OpenCV 3. Meet Your Experts: We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: Robert Laganiereis 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, driver-assistance, object detection, and tracking. Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development, published by McGraw Hill in 2001. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of startups companies such as Cognivue Corp, iWatchlife, and Tempo Analytics. AdiShavitis an experienced software architect and has been an OpenCV user since it was in early beta back in 2000. Since then he has been using it pretty much continuously to build systems and products ranging from embedded, vehicle, and mobile apps to desktops and large, distributed cloud-based servers and services. His specialty is in computer vision, image processing, and machine learning with an emphasis on real-time applications. He specializes in cross-platform, high performance software combined with a high production-quality maintainable code base. He builds many products, apps, and services that leverage OpenCV.
