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.
- 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
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+
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
