Artificial vision systems in control structures

Course Code: SEIA201_1 • Study year: II • Academic Year: 2022-2023
Domain: Electronic engineering, telecommunication and information technologies - Masters • Field of study: Advanced intelligent electronic systems
Type of course: Compulsory
Language of instruction: English
Erasmus Language of instruction: English
Name of lecturer: Manuella Kadar
Seminar tutor: Manuella Kadar
Form of education Full-time
Form of instruction: Class / Seminary
Number of teaching hours per semester: 42
Number of teaching hours per week: 3
Semester: Autumn
Form of receiving a credit for a course: Grade
Number of ECTS credits allocated 6

Course aims:

- Knowledge of the fundamentals (concepts, principles and theories) regarding systems based on artificial vision.
- Use of techniques and algorithms in the field of systems based on artificial vision with application in industrial manufacturing processes, automotive and medical;
- Acquisition of knowledge about software packages in systems based on artificial vision (OpenSource ComputerVision, Python).

Course Entry Requirements:


Course contents:

1. Basic concepts of artificial vision systems. Flexible production systems. Process control. Algorithms and heuristics. 2. Artificial techniques and methods. Representation of images. Basic image processing functions. Feature extraction. Character extraction techniques. Advanced Hough transformation techniques and active contour control. Feature description for feature recognition analyzes. 3. Intelligent image processing. Interactive image processing. Syntactic and symbolic analysis and interpretation of images. Image restoration. Weiner filter. Restoration with maximum entropy. 4. 3D images. Calibration, epipolar constraints, coordinate systems. Active and passive classification systems. Morphology. Binary image processing systems and image geometry. 5. Multi-camera systems. Multi-plex video systems. Network artificial vision systems. Reconstruction of process interruption. 6. Control of external devices. Devices and signals. Protocols. Flexible lighting and control systems. Mechanical drive. Lenses. Calibration. Visual control of redundant robot arms.

Teaching methods:

Ppt presentation, discussions, case studies

Learning outcomes:

-Learning techniques in systems based on artificial vision -Learning the principles of development and application of systems based on artificial vision -Practicing techniques based on artificial vision in industrial applications -Completion of functional systems based on artificial vision

Learning outcomes verification and assessment criteria:

Final evaluation, written work 50% 50% check during the semester

Recommended reading:

Nixon, MS and Aguado, AS, Feature Extraction and Image Processing in Computer Visio, Butterworth Heinmann (Newnes) 3rd Edition , Soton , 2012 , -
Simon Prince, Computer Vision: Models, Learning, and Inference, Cambridge , Cambridge , 2011 , 566
Roy Davies, Computer and Machine Vision: Theory, Algorithms, Practicalities, Academic Press , Elsevier , 2012 , 912