ID:
511899
Duration (hours):
45
CFU:
6
SSD:
BIOINGEGNERIA INDUSTRIALE
MISURE MECCANICHE E TERMICHE
Year:
2025
Overview
Date/time interval
Primo Semestre (29/09/2025 - 16/01/2026)
Syllabus
Course Objectives
The primary objective of the course is to provide a solid theoretical and practical foundation on state-of-the-art computer vision systems, with particular emphasis on applications in industrial and bioengineering contexts. The course offers an interdisciplinary pathway integrating concepts from optics, sensing technologies, image analysis, and artificial intelligence, aimed at equipping students with the skills to design and implement measurement systems based on digital images. By the end of the course, students will be able to:
- Understand the physical and technical principles behind image formation, camera and lens operation, and light propagation in measurement systems.
- Select, configure, and calibrate an image-based measurement chain, including cameras, lenses, illumination systems, and analysis software.
- Apply key techniques for preprocessing and analyzing digital images, using both analytical algorithms and artificial intelligence methods in industrial and biomedical scenarios.
- Critically assess the metrological performance of a vision system and propose appropriate technical solutions for experimental needs.
- Understand and use AI-based vision systems for the measurement and quantification of physical quantities.
- Design, document, and communicate an independent experimental project using appropriate scientific terminology.
- Understand the physical and technical principles behind image formation, camera and lens operation, and light propagation in measurement systems.
- Select, configure, and calibrate an image-based measurement chain, including cameras, lenses, illumination systems, and analysis software.
- Apply key techniques for preprocessing and analyzing digital images, using both analytical algorithms and artificial intelligence methods in industrial and biomedical scenarios.
- Critically assess the metrological performance of a vision system and propose appropriate technical solutions for experimental needs.
- Understand and use AI-based vision systems for the measurement and quantification of physical quantities.
- Design, document, and communicate an independent experimental project using appropriate scientific terminology.
Course Prerequisites
A basic understanding of the fundamental principles of Physics and Mathematical Analysis is required.
Teaching Methods
The course is structured into lectures, guided hands-on sessions, and, optionally, project work activities. The lectures, supported by slide presentations available to the students, provide the theoretical and methodological framework for understanding and developing image-based measurement systems. Presentations are enriched with real-world examples. The practical sessions, held both in class and in the laboratory, allow students to gain familiarity with software tools (e.g., OpenCV, Python) and apply the learned techniques to the design of computer vision systems aimed at solving real engineering problems. Students may also engage in project work, individually or in small groups, focused on the design and prototyping of simple computer vision systems, with potential applications in industrial, clinical, diagnostic, or rehabilitation settings. The course may include invited seminars delivered by experts from academia or industry. Attendance to lectures and practical sessions is strongly recommended to fully benefit from the course activities.
Assessment Methods
The final assessment consists of an individual oral exam, which is mandatory for all students. Students may also choose to complete an optional project work, individually or in small groups. If completed, the project will be discussed during the oral exam and will contribute to the final grade, in alignment with the course objectives.
1 - Oral exam (mandatory) An individual interview lasting approximately 20–30 minutes, assessing:
- understanding of the theoretical concepts covered in the course;
- ability to connect theory with practical applications;
- clarity of exposition and appropriate use of scientific terminology;
- optional critical reflection on the project work, if completed.
No materials are allowed during the oral exam.
2 - Project work (optional) Students opting for the project work will design, implement, and document an experimental computer vision system for a specific application. The project will be presented and discussed during the oral exam. Evaluation will consider:
- methodological accuracy and clarity;
- technical quality of the developed system;
- critical analysis of results;
- originality and design complexity.
The final grade, expressed out of 30, will be based on the oral exam and, if applicable, the project work contribution. Both components must be passed (≥18/30) in order to pass the course. For the project presentation, slides, videos, images and demos are allowed.
1 - Oral exam (mandatory) An individual interview lasting approximately 20–30 minutes, assessing:
- understanding of the theoretical concepts covered in the course;
- ability to connect theory with practical applications;
- clarity of exposition and appropriate use of scientific terminology;
- optional critical reflection on the project work, if completed.
No materials are allowed during the oral exam.
2 - Project work (optional) Students opting for the project work will design, implement, and document an experimental computer vision system for a specific application. The project will be presented and discussed during the oral exam. Evaluation will consider:
- methodological accuracy and clarity;
- technical quality of the developed system;
- critical analysis of results;
- originality and design complexity.
The final grade, expressed out of 30, will be based on the oral exam and, if applicable, the project work contribution. Both components must be passed (≥18/30) in order to pass the course. For the project presentation, slides, videos, images and demos are allowed.
Texts
Szeliski R., Computer Vision: Algorithms and Applications, 2a ed., Springer, 2022, ISBN: 978-3-030-34371-8 Versione elettronica disponibile per uso personale sul sito ufficiale dell'autore: https://szeliski.org/Book
Contents
- Fundamentals of geometric optics and image formation.
- Vision systems: lenses and image sensors. - Illumination systems.
- Optical microscopy and illumination techniques.
- Image pre-processing and analysis.
- Calibration and triangulation for dimensional measurements.
- Artificial intelligence for image analysis: from classification to referring image segmentation.
- AI model deployment for computer vision on embedded devices.
- Advanced imaging techniques: thermographic, multispectral, hyperspectral, polarimetric, and telecentric imaging.
- Vision systems: lenses and image sensors. - Illumination systems.
- Optical microscopy and illumination techniques.
- Image pre-processing and analysis.
- Calibration and triangulation for dimensional measurements.
- Artificial intelligence for image analysis: from classification to referring image segmentation.
- AI model deployment for computer vision on embedded devices.
- Advanced imaging techniques: thermographic, multispectral, hyperspectral, polarimetric, and telecentric imaging.
Course Language
Italian
Degrees
Degrees (2)
Bioengineering
Master’s Degree
2 years
INDUSTRIAL AUTOMATION ENGINEERING
Master’s Degree
2 years
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