Building Computer Vision Projects with OpenCV4 and CPlusPlus

Implement complex computer vision algorithms and explore deep learning and face detection This course is your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. By taking this course, you will be able to work on complex projects that involves image processing, motion detection, and image segmentation.

What you will learn

• Explore algorithmic design approaches for complex computer vision tasks
• Work with OpenCV's most updated API through projects
• Understand 3D scene reconstruction and Structure from Motion
• Study camera calibration and overlay AR using the ArUco Module
• Create CMake scripts to compile your C++ application
• Explore segmentation and feature extraction techniques
• Remove backgrounds from static scenes to identify moving objects for surveillance
• Work with new OpenCV functions to detect and recognize text with Tesseract

Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

• Processor: 2.6 GHz or higher, preferably multi-core
• Memory: 4GB RAM
• Hard disk: 10GB or more
• An Internet connection
• macOSX machine (for example, MacBook, iMac) running macOS High Sierra v10.13+

Software requirements

You’ll also need the following software installed in advance:

• Operating System: Windows (8 or higher)
• Qt
• OpenGL
• Tesseract
• Browser: Latest version of one or more browsers is recommended