English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 0h 54m | 301 MB
eLearning | Skill level: All Levels
An expert’s guide to build computer vision applications
OpenCV 3 is a native cross-platform C++ Library for computer vision, machine learning, and image processing. This impressive API also makes starting OpenCV 3 projects a daunting prospect. Each video in this course provides a practical and innovative approach so you’ll be able to choose wisely in your future projects. It will help you tackle increasingly challenging computer vision problems that you may face in your career. Each of the examples have been battle-tested in the author’s industry research.
You’ll 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’ll learn about video stabilization, background video monitoring, and subtraction. Moving ahead, you’ll find out about object detection robot vision, where you’ll match image descriptors. You’ll also get an overview of image warping and the perspective transform, and will use homographies to warp images.
Finally, explore into Artificial Intelligence with Deep Neural Networks and you’ll get a taste of how DNNs can be used within OpenCV. You’ll see how to install and load DNN models and classify images. At the end of the course, you’ll discover how to convert low-level pixel information to high-level concepts for applications such as object detection, recognition, and scene monitoring.
What You Will Learn
- Understand video stabilization and background video monitoring and subtraction
- See how can we identify known objects in a video
- Get to know about homographies so you can warp images
- Understand how Deep Neural Networks can be used within OpenCV3
- Familiarize yourself in Image Recognition with DNNs
01 The Course Overview
02 Video Stabilization Basics
03 Stabilizing Videos
04 Background Subtraction
05 Image Features
06 Image Feature Descriptors
07 What Are Deep Neural Networks
08 Loading DNN Models
09 Object Detection