Image Understanding II: Robot Vision
Module IN2016
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 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 workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 120 h | 45 h | 4 CP |
Content, Learning Outcome and Preconditions
Content
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
- 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.
Preconditions
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
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
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 3 | Image Understanding II: Robot Vision (IN2016) | Steger, C. |
Fri, 08:30–10:00, Interims I 102 Mon, 16:15–17:45, Interims II 003 and singular or moved dates |
eLearning documents |
Learning and Teaching Methods
Lecture (presentation of slides and interactive examples)
Media
PowerPoint
Literature
Carsten Steger, Markus Ulrich, Christian Wiedemann: Machine Vision
Algorithms and Applications; Wiley-VCH, Weinheim, 2007
Algorithms and Applications; Wiley-VCH, Weinheim, 2007
Module Exam
Description of exams and course work
The exam takes the form of a written 60 minutes 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
The exam may be repeated at the end of the semester.