Image and Video Processing

The course will expose students to all stages of the image and video processing pipeline. Starting from acquisition, the students will learn how images are captured and represented. They will learn about sampling theory, different image and video formats, and the basics of color theory. Next, the students will be introduced to various image and intensity transformations as well as filtering. The course will discuss Fourier transform and concepts such as convolution, high- and low-pass filter in both primary and frequency domains. The above topics will build the foundation for more advanced topics in image and video processing, such as restoration and enhancement. To this end, the course will also discuss different image decomposition techniques such as Gaussian, Laplacian pyramids, wavelet transform, and more advanced filtering strategies such as cross-bilateral filtering. Final lectures will introduce students to the most recent developments in image and video processing, which involve machine learning techniques. The students will learn about basic techniques which exploit neural networks in the context of image and video processing.

From robotics, medical, and surveillance applications to telepresence, displays, and entertainment, cameras and imaging techniques are omnipresent and part of many information systems. This course will provide theoretical and practical foundations regarding the acquisition and processing of image and video content. It will give students the basic knowledge and skills necessary to develop and implement systems that use images either as a critical source of information or ways to communicate and visualize digital information.

In presence

The course is a series of lectures interleaved with interactive classes dedicated to discussing assignments and theoretical exercises. The assignments consist of practical tasks, i.e., implementation of techniques discussed during lectures.

The final grade is a result of the grades from the assignments and the final exam.