OpenCV is one of the best open source computer vision libraries available to developers. With OpenCV, developers can create complete projects for image processing, object detection, and motion detection. This course is for absolute beginners who wish to learn how to build openCV projects from scratch with working code samples. We will begin with the introduction on computer vision and its basic concepts such as filtering, histograms, Object segmentation, and object detection. As you progress through the course you will then dig deeper into image processing exploring various computer vision algorithms and understand how the latest advancement in machine learning and deep learning enhances the process of object detection. You will put this knowledge to practice by building real-world computer vision applications as you progress through the course. Later you will get acquainted with the API functionality of OpenCV and gain insights into design choices in a complete computer vision project. You'll also go beyond the basics of computer vision to implement solutions for complex image processing projects such as skin color analysis, face landmark and pose estimation, Augmented reality applications, and Number plate recognition. Finally towards the end of the course you will learn about certain best practices and common pitfalls to avoid while building computer vision applications.
- We will begin with the introduction on computer vision and its basic concepts such as filtering, histograms, Object segmentation, and object detection.
- Dig deeper into image processing exploring various computer vision algorithms and understand how the latest advancement in machine learning and deep learning enhances the process of object detection.
- Put this knowledge to practice by building real-world computer vision applications as you progress through the course.
- Get acquainted with the API functionality of OpenCV and gain insights into design choices in a complete computer vision project.
- Implement solutions for complex image processing projects such as skin color analysis, face landmark and pose estimation, Augmented reality applications, and Number plate recognition.
- Learn about certain best practices and common pitfalls to avoid while building computer vision applications.
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