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Computer vision I
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Computer vision I
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Academic year 2023/2024
- Course ID
- NEU0279A
- Teacher
- Pietro Cipresso (Lecturer)
- Year
- 2nd year
- Teaching period
- Second semester
- Type
- Distinctive
- Credits/Recognition
- 2
- Course disciplinary sector (SSD)
- M-PSI/03 - psychometrics
- Delivery
- Formal authority
- Language
- English
- Attendance
- Optional
- Type of examination
- Written and oral
- Type of learning unit
- modulo
- Modular course
- Computer vision (NEU0279)
- Prerequisites
- Knowledge of discrete mathematics, calculus, and Matlab programming is considered welcome and useful but not a prerequisite.
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Sommario del corso
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Course objectives
The Computer Vision I module will provide the general information and tools for computer vision methods with regards to photogrammetry and spatial recordings (from drone to 3D laser scanners), and the human body (including body scanners and mobile devices). It is difficult to imagine working with 3D models consisting of thousands of points without appropriate visualization. Therefore, graphical tools, such as 3D model construction (e.g. organ segmentation), the interactive definition and validation of metrics and the visualization of the results (e.g. showing the registered 3D models on top on the raw image data) will be presented to facilitate the understanding of these techniques. Virtual reality will be part of the visualization techniques with regards to spaces, objects and human bodies (including rigging).
Special emphasis will be given on computation data analyses including statistical data analyses and artificial intelligence techniques, including big data, deep learning and blockchain to ensure a secure data exchange among the different actors involved in these processes.
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Results of learning outcomes
The student will be able to record and create 3D model in digital space by computing multiple images or volume-data of objects, spaces, and human bodies. Moreover, the students will be able to harmonize data to make original raw scan data treatable by the multivariate analyses that will render shape information exploitable in 3D models (also to be visualized in virtual reality settings). Finally, students will be able to implement data protection protocols to process secure exchange of 3D data in accordance to GDPR and European legal restrictions.
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Program
Computer Vision I consists on description of recording of 3D spaces, objects, and human bodies; definition and description of photogrammetry; analyses of 3D data processing software; definition of data handling, aggregation and anonymization software; definition of processes for obtaining homodels (and anthropometric model and measurements) from 3D body-surface scanners; understanding of graphic tools (including virtual reality tools) enabling shape data exploration; definition of processes and protocols for data protection and to secure blockchain data exchange.
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Course delivery
Class lectures, seminars, and thematic workshops.
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Learning assessment methods
The final test will consist in one practical exercise and an oral examination.
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Support activities
Practical workshops and lectures from international experts.
Suggested readings and bibliography
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Liu, S., Zhang, M., Kadam, P., & Kuo, C. C. J. (2021). 3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods. Springer.
Durá Gil, J. V., Remon, A., Rodriguez, I. M., Pariente-Lobo, T., Salmeron-Majadas, S., Perrone, A., ... & Cipresso, P. (2022). 3D Human Big Data Exchange Between the Healthcare and Garment Sectors. In Technologies and Applications for Big Data Value (pp. 225-252). Springer, Cham.
Toti, M., Tuena, C., Semonella, M., Pedroli, E., Riva, G., & Cipresso, P. (2019, April). Anthropometry and Scan: A Computational Exploration on Measuring and Imaging. In International Symposium on Pervasive Computing Paradigms for Mental Health (pp. 102-116). Springer, Cham.
Pedroli, E., Digilio, R., Tuena, C., Durá-Gil, J. V., Cernigliaro, F., Riva, G., & Cipresso, P. (2018, January). The use of 3D body scanner in medicine and psychology: A narrative review. In International Symposium on Pervasive Computing Paradigms for Mental Health (pp. 74-83). Springer, Cham.
Gaggioli, A., Eskandari, S., Cipresso, P., & Lozza, E. (2019). The middleman is dead, long live the middleman: the “trust factor” and the psycho-social implications of blockchain. Frontiers in Blockchain, 2, 20.
ISO 7250-1:2017—Basic human body measurements for technological design – Part 1: Body measurement definitions and landmarks. (2017). https://www.iso.org/standard/65246.html
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