Signal and Image Processing

Abstract

The objective of this lecture is to introduce the basic concepts of image formation, and explain the methods commonly used in Computer Vision applications. To fully understand the presented computer vision methods the necessary signal processing background will be taught.

Objective

This lecture aims to give an overview of the basic concepts of image formation, preception & analysis, and Computer Vision.

Content

In the introductory and motivation part of the course an overview of emerging computer vision applications is given. The next part then introduces several concepts related to digital images and some of the notation used throughout the lecture. Furthermore it briefly summarises the mechanics of the human visual system, and introduces an image model based on the illumination-reflection phenomenon. The third part deals with the signal processing background necessary to fully understand the underlying mathematics behind many Computer Vision algorithms. In order for computer to be able to process an image, the images have to be described as a series of numbers, each of finite precision. An individual part is devoted to these basic concepts. Part five and six of this lecture a fully devoted to image enhancement and image restoration techniques. It principle objective of these enhancement techniques is to process the images so that the results are more suitable than the original images for a specific application. Part seven concentrates on the extraction of basic features from the images, whereas the succeeding part concentrates on the segmentation of bigger structures from the image data. Finally, an overview of the existing texture segmentation techniques is given.

Resources