Image Understanding II: Robot Vision

Module IN2016

This Module is offered by TUM Department of Informatics.

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Basic Information

IN2016 is a semester module in German or English language at Bachelor’s level und Master’s level which is offered in winter semester.

This Module is included in the following catalogues within the study programs in physics.

  • Catalogue of non-physics elective courses
Total workloadContact hoursCredits (ECTS)
120 h 45 h 4 CP

Content, Learning Outcome and Preconditions


The lecture provides a detailed description of methods and algorithms that are relevant in practice for solving robot vision and machine vision applications. The main focus of the lecture is the description of the methods and their basic principles. Practical examples show the typical applications in which the presented methods are used. The topics topics that are covered include: - Feature extraction - Classification - Color image processing - Identification (bar codes, OCR) - Hand-eye calibration - Object recognition

Learning Outcome

Participants of this lecture will have a thorough understanding of the theory, data structures, and implementation of the most important algorithms that are used in robot vision and machine vision. They are capable of analyzing and evaluating machine vision tasks and can use their knowledge and skills to create robot vision and machine vision applications.


IN2023 Image Understanding I: Machine Vision Algorithms; basic knowledge of the following fields: - linear algebra (linear transformations between vector spaces expressed in matrix algebra) - calculus (in particular, sums and differentiation and integration of one- and two-dimensional functions) - Probability theory

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

VO 3 Bildverstehen II: Robot Vision (IN2016) Freitag, 16:00–17:30
Montag, 08:30–10:00
Montag, 08:30–10:00
sowie einzelne oder verschobene Termine

Learning and Teaching Methods

Lecture (presentation of slides and interactive examples)




Carsten Steger, Markus Ulrich, Christian Wiedemann: Machine Vision Algorithms and Applications; Wiley-VCH, Weinheim, 2007

Module Exam

Description of exams and course work

The exam takes the form of written test. Application questions assess the ability to analyze, evaluate, and create realistic robot vision and machine vision applications. Knowledge questions assess the acquaintance with the algorithms of robot vision and machine vision as well as the suitability of the choice of algorithms to solve a particular application.

Exam Repetition

There is a possibility to take the exam at the end of the semester.

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