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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