Course

IBV - Industrial Image Processing

PDF Course Catalog Deutsche Version: IBV

Version: 2 | Last Change: 23.09.2019 09:14 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben

Long name Industrial Image Processing
Approving CModule IBV_BaET, IBV_BaTIN
Responsible
Prof. Dr. Lothar Thieling
Professor Fakultät IME
Level Bachelor
Semester in the year summer semester
Duration Semester
Hours in self-study 78
ECTS 5
Professors
Prof. Dr. Lothar Thieling
Professor Fakultät IME
Requirements basic skills in signal processing
basic skills in Java and/or C
basic skills in analysis and linear algebra
Language German
Separate final exam Yes
Literature
Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Prentice Hall
Scott E Umbaugh, COMPUTER VISION and IMAGE PROCESSING: A Practical Approach Using CVIPtools, Prentice Hall
Wolfgang Abmayer, Einführung in die digitale Bildverarbeitung,Teubner
Final exam
Details
The students should demonstrate the following competences in an oral exam: 1.) Safe handling of basic concepts and mechanisms. 2.) Analyze problems in the field of industrial image processing and solve them with suitable methods. 3.) Analyze existing solutions and explain the used algorithmic and theory.
Minimum standard
At least 50% of the total number of points
Exam Type
The students should demonstrate the following competences in an oral exam: 1.) Safe handling of basic concepts and mechanisms. 2.) Analyze problems in the field of industrial image processing and solve them with suitable methods. 3.) Analyze existing solutions and explain the used algorithmic and theory.

Learning goals
Knowledge
image construction, global image properties, and access to image data
graylevel and color images
global image properties,
mean value, variance, entropy
histogram, cumulative histogram
development environment
software design tools
compiler
linker
debugger
softwaretools for image processing and image analysis
softare-based access to image data and parameters
overview of the available ip-modules (moduls dor image processing and image analysis)
design and implementation of own ip-moduls
design of algorithmic chains based on ip-modules using visual programming
gray level transformation
linear gray level transformation, histogram spreading
non-linear gray level transformation
histogram equalization
local histogram equalization
look-up-table
analysis and processing of color images
technical and human color perception
additive and subtractive color mixing
RGB color space
HSI color space
transformation RGB to HSI and vise versa
rank-order operators (non-linear filtering)
max, min, median
morphologische Operatoren
erosion, dilation
opening, closing
locating structures
analysis and processing in frequency domain
fourier analysis and synthesis of one-dimensional digital signals
real spectrum, imaginary spectrum
amplitude spectrum, phase spectrum
filtering in frequency domain
fourier analysis and synthesisf of images
real spectrum, imaginary spectrum
amplitude spectrum, phase spectrum
filtering in spatial domain
non directional filter
directional filter
inverse filtering
linear filtering in spatial domain
convolution, convolution, transfer function
typical convolution maks (mean, gauß, differencial-operator, sobel-operator, laplace-operator)
gradient and its calculation using differential-operator and sobel-operator
analysis and evaluation of the operator in the frequency domain
Tracking
normalized cross-correlation
without prediction
with prediction (kalman filter)
measuring of subpixel edges
one-dimensional
two-dimensional using gradient
Skills
the presented methods for image enhancement can be
named
described
delineated in terms of application areas
evaluated in terms of advantages and disadvanteges
problemspecific parameterized
the presented color spaces and corresponding algorithms can be
named
described
delineated in terms of application areas
evaluated in terms of advantages and disadvanteges
problemspecific parameterized
the presented methods for non liniar filtering can be
named
described
delineated in terms of application areas
evaluated in terms of advantages and disadvanteges
problemspecific parameterized
Spectra of images and / or convolution masks can be
analyzed
designed
discussed
the presented methods for linear filtering can be (space and frequency domain)
named
described
delineated in terms of application areas
evaluated in terms of advantages and disadvanteges
problemspecific parameterized
Expenditure classroom teaching
Type Attendance (h/Wk.)
Lecture 2
Exercises (whole course) 0
Exercises (shared course) 0
Tutorial (voluntary) 0
Special literature
keine/none
Special requirements
1.) Develop programs to solve specific problems. 2.) Problem solving competence in the field of linear algebra and analysis. 3.) Representation of time-discrete signals in the time and frequency domain (DFT).
Accompanying material
lecture foils (electronic)
tool chain for image processing
self-study tutorials for the tool chain
Separate exam
none

Learning goals
Skills
purposeful handling of the tool chain for image processing
deal with complex tasks in a small team
derive complex solutions that can be implemented using image processing and image analysis
Expenditure classroom teaching
Type Attendance (h/Wk.)
Practical training 2
Tutorial (voluntary) 0
Special literature
keine/none
Special requirements
1.) Develop programs to solve specific problems. 2.) Problem solving competence in the field of linear algebra and analysis. 3.) Representation of time-discrete signals in the time and frequency domain (DFT).
Accompanying material
problem and task description (electronic)
tool chain for image processing
self-study tutorials for the tool chain
Separate exam
none

Impressum
Datenschutzhinweis
Haftungshinweis
Bei Fehlern, bitte Mitteilung an die
Webredaktion der Fakultät IME

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