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 |
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