Course­ Manual IBA

Industrial Computer Vision


PDF Course Catalog Deutsche Version: IBA

Version: 4 | Last Change: 23.09.2019 09:14 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben

Long name Industrial Computer Vision
Approving CModule IBA_BaET, BV2_BaMT, IBA_BaTIN
Responsible
Prof. Dr. Lothar Thieling
Professor Fakultät IME
Valid from summer semester 2023
Level Bachelor
Semester in the year winter 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 computer vision 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 and access to image data
grey-level image and colour image
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 segmentation
histogram-based segmentation
histogram analysis
shading and its compensation
region-based segmentation
filling
split and merge
region growing
contour-based segmentation
contour tracking
hough-transformation
Knowledge feature extraction
geometric features
basic features (area, perimeter, shape factor)
central moments
normalized central moments
polar distance
curvature
DFT of polar distance and curvature
color features (HSI)
texture features
co-occurrence matrix
haralick features
Knowledge Klassifikation von Merkmalen
terms and concepts
feature vector, feature space, object classes
supervised / unsupervised classification
learning / not learning classification
typical methods
quader method
minimum distance
nearest neighbour
maximum likelihood
neuronale Netze
the artificial neuron as a simple classifier
operation
activation function
bias
training a neuron (gradient descent)
multi-layer-perceptron
operation
purposes of the layers
backpropagation training algorithm
development environment for creating and training neural networks
design and configuration of neural networks
training neural networks
verification tof rained networks
generating C-functions from trained networks
Skills the presented methods for segmentation can be
named
described
delineated in terms of application areas
evaluated in terms of advantages and disadvanteges
problemspecific parameterized
Skills the presented methods for feature extraction can be
named
described
delineated in terms of application areas
evaluated in terms of advantages and disadvanteges
problemspecific parameterized
Skills the presented methods for scallsification can be
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
fundamentals in image processing
Accompanying material lecture foils (electronic), tool chain for computer vision, self-study tutorials for the tool chain
Separate exam No

Learning goals
Goal type Description
Skills purposeful handling of the tool chain for computer vision
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
fundamentals in image processing
Accompanying material problem and task description (electronic), tool chain for computer vision, self-study tutorials for the tool chain
Separate exam No

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