Overview
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 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.
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
Assessment
Final evaluation, written work 50% 50% check during the semester