Course­ Manual 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
Valid from winter semester 2022/23
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 EN mündliche Prüfung, strukturierte Befragung

Learning goals
Goal type Description
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
Knowledge gray level transformation
linear gray level transformation, histogram spreading
non-linear gray level transformation
histogram equalization
local histogram equalization
look-up-table
Knowledge 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
Knowledge rank-order operators (non-linear filtering)
max, min, median
morphologische Operatoren
erosion, dilation
opening, closing
locating structures
Knowledge 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
Knowledge 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
Knowledge Tracking
normalized cross-correlation
without prediction
with prediction (kalman filter)
Knowledge 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
Skills 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
Skills 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
Skills Spectra of images and / or convolution masks can be
analyzed
designed
discussed
Skills 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 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 No

Learning goals
Goal type Description
Skills purposeful handling of the tool chain for image processing
Skills deal with complex tasks in a small team
Skills 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 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 No

Bei Fehlern, bitte Mitteilung an die
Webredaktion der Fakultät IME

© 2022 Technische Hochschule Köln