Basic Mathematical Tools for Imaging and Visualization - IN2124

Administrative Info Lecture: PD Dr. Tobias Lasser Exercises: Anca Stefanoiu, Rüdiger Göbl, Walter Simson IN2124: 2+2 SWS, 5 ECTS The course is held in English. Contact Contact by email Moodle course	Time & Location Lecture and exercise: Monday, 16:05 - 17:35, room Interims Hörsaal 2 Wednesday, 15:35 - 17:05, room MW 1801 Exam: Wednesday, February 27, 2019, 10:30 - 11:45 Repeat exam: Thursday, April 18, 2019, 15:30 - 16:45
Site Content Overview Course materials Literature	Announcements The first session on October 17, 2018, will be a short introduction on the modalities of the course and on its contents. The first regular session will be on Monday, October 22, 2018. The first session will take place on Wednesday, October 17, 2018.

Overview

For the development and application of many image processing techniques and computer vision algorithms, a deep mathematical understanding is of fundamental importance. This course aims to provide interested students with a solid mathematical basis for imaging and visualization applications.

Course materials

All course materials can be found via the Moodle course.

Literature

MATLAB

• Cleve Moler, first chapter of Numerical Computing with MATLAB, SIAM (You can download the book for free!)

Wolfram Alpha

• For quickly calculating, plotting, or learning something you might want to have a look at Wolfram Alpha.

Linear Algebra

• Yousef Saad, Iterative Methods for Sparse Linear Systems, SIAM (You can download the book for free!)
• Gilbert Strang, Introduction to Linear Algebra, Wellesley-Cambridge Press (Available in the library! Check out his video-lectures on youtube!)
• Lloyd N. Trefethen and David Bau, Numerical Linear Algebra, SIAM (Available in the library!)

Analysis

• Walter Rudin, Real and Complex Analysis, McGraw-Hill (Available in the library!)

Optimization

• Ake Björck, Numerical Methods for Least Squares Problems, SIAM (Available in the library!)
• Jonathan Shewchuk, An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
• Uri Ascher, A first course in numerical methods, SIAM (Available in the library!)

Probability Theory

• Heinz Bauer, Measure and Integration Theory, deGruyter (Available in the library!)
• Sheldon Ross, Introduction to probability and statistics for engineers and scientists, Elsevier (Available in the library!)