LEARNING PATH: OpenCV: Computer Vision with OpenCV 3

OpenCV is a library of programming functions mainly aimed at real-time computer vision. In simple language, it is one of the most powerful library used for image processing and for building computer vision applications. If you wish to build computer vision systems that are smarter, faster, more complex, and more practical with OpenCV 3, then you should surely go for this Learning Path.
Packt’s Video Learning Path is 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:
●       Get acquainted with OpenCV 3 for photo filtering and photo manipulation through practical, real-world examples
●       Dive into video surveillance tools to perform video content analysis
●      Explore the use of deep neural networks in computer vision applications
Let’s take a quick look at your learning journey. This Learning Path starts off with an introduction to OpenCV 3 wherein you will start a new project from scratch and see how to load an image file and display it. You will then jump into the next project named project Photo Fix App wherein you will learn how to interactively adjust image brightness and contrast. You will also learn how to add sliders to display window and how to react to slider events.
Next, you will  move on to another project named OpenCV 3 – Miniaturizing APP wherein you will learn how to add miniaturizing tilt-shift effect and learn about blurring images, compositing, and handling UI mouse events. You will then learn how to apply Instagram-like color ambiance filters to images and see how to load videos, apply the filters to them, and store them in the project named Color Ambience APP. You will also learn to create image editing tools and effects that appear to magically work and explore the secrets of creating HDR images.
Moving ahead, you will deep dive into video surveillance tools, such as wildlife camera traps, extreme sports cameras, and closed circuit video cameras. Many applications require video content analysis, so you will learn about video stabilization, background video monitoring, and subtraction. You will learn about object detection robot vision where you will match image descriptors. You will also be glanced through image warping and the perspective transform and will learn to use homographies to warp images. Next, you will explore artificial intelligence (AI) with deep neural networks (DNNs) and will understand how DNNs can be used within OpenCV.  Finally, you will discover how to convert low-level pixel information to high-level concepts for applications such as object detection, recognition, and scene monitoring.
By the end of this Learning Path, you will be able to tackle increasingly challenging computer vision problems faced in day-to-day life and build computer vision systems that are smarter, faster, more complex, and more practical. 
Meet Your Expert(s):
We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:
● Adi Shavit is 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, through desktops to large, distributed cloud-based servers and services. His specialty is computer vision, image processing, and machine learning with an emphasis on real-time applications. The technology he is interested in includes Advanced C++ (C++17 an upward), Deep Learning (Torch, Caffe, TensorFlow, tiny-dnn), OpenVX, GPU (CUDA, OpenCL), graphics (OpenGL, Vulcan), robotics (ROS), functional programming (Haskell, Elm, Idris), and IoT (Arduino, NodeMCU). He specializes in cross-platform, high-performance software combined with high production quality maintainable code bases. He builds many products, apps, and services that leverage OpenCV.