Image Processing II: Algorithms and Applications

The solution of an image processing task usually consists of several interrelated steps, such as preprocessing, object segmentation and feature extraction, with the aim of reliably detecting characteristic properties of a test object. In the case of an automatic test or classification, these features can be used to obtain information about the object condition or the type of object. For this purpose, among other things, algorithms for pattern recognition, methods for three-dimensional object reconstruction (e.g. stereo vision, triangulation methods) and the fundamentals of machine learning are developed and applied. In this course, various methods and algorithms for the IT analysis of pixel data up to a statement about the quality of a test object are presented and the interaction of the sub-steps is illustrated using practical examples.