The first class in the Computer Vision training starts off by giving a good introduction on what Computer Vision is, its applications, and its relation to the human visual system. We then move to the theoretical part in which we learn how digital images are represented and processed using basic operations such as thresholding, convolutions, and filters.
These concepts will come together in the second part of the training, in which we discuss Canny Edge Detection and its relation to concepts such as Gaussian kernels, gradient computation, non-max-suppression, and hysteresis. We then dive into Scale Invariant Feature Transformation (SIFT), a powerful technique for identifying key feature points, often used in image stitching and conclude by providing a list of commonly used Computer Vision tools and resources.
The training includes theory, demos, and hands-on exercises.
After this training you will have gained knowledge about:
- Computer Vision and its applications
- Reading digital images and their representation
- Transformation operations such as thresholding
- Filters and convolutions
- Gradients computation
- Canny Edge Detection
- Scale Invariant Feature Transform (SIFT)
- Computer Vision tools