- Course
- 2024-2025
- Semester
- 1
- ECTS
- 6
- Type
- Compulsory
- University
- UPorto and USC
Subject objectives
To understand the basics and fundamentals of a computer vision system for different types of sensors and their corresponding applications.
Contents
Chapter 1- Image and Video Acquisition
Chapter 2- Smart Image Sensors
Chapter 3- Machine Vision Algorithms
Chapter 4- Geometric Camera Calibration
Chapter 5- 3D Data Acquisition
Chapter 6- Industrial Machine Vision Systems
Chapter 7- Industrial Machine Vision Applications
Basic and complementary bibliography
Basic Bibliography
Richard Szeliski. «Computer Vision: Algorithms & Applications». Springer 2010.
Rafael C. González, Richard E. Woods. «Digital Image Processing». Third Edition. Pearson 2007.
Jun Ohta. «Smart Image Sensors and Applications». Second Edition. CRC Press 2020.
Junichi Nakamura. «Image Sensors & Signal Processing for Digital Still Cameras». CRC Press 2016.
Alexander Hornberg, «Handbook of Machine and Computer Vision: The Guide for Developers and Users», Wiley-VCH, 2017.
E. R. Davies, «Machine Vision, Theory, Algorithms, Practicalities», Academic Press, 2012.
Adrian Kaehler, Gary Bradsky, «Learning OpenCV 3», O’Reilly Media Inc., 2017.
Laurent Berger, «Traitement d’images et de vidéos avec OpenCV 4 en Python (Windows, Linux, Raspberry)», Éditions D-BookeR, 2020.
Complementary Bibliography
Eric R. Fossum, Nobukazu Teranishi, Albert Theuwissen, David Stoppa, Edoardo Charbon. «Photon-Couting Image Sensors», MDPI 2017.
Image Sensors World (Internet Blog)- http://image-sensors-world.blogspot.com/
Competencies
Basic Skills
CB7- Students have to be capable of applying the knowledge learned throughout the course and problem-solving abilities in new scenarios related to their field of study.
Transversal Skills
CT2- Team work capacity, organization and planification.
CT5- Green and sostenibility plans in their professional careers. Equity, responsability and efficiency with the resources available.
Generic Skills
CG3- Ability to design and deploy computer vision systems meeting existing needs, and ability to run the most suitable tools.
CG4- To be critic and to make strict and serious assessment of technologies and methodologies.
Specific Skills
CE6- To be knowledgeable and to apply fundamentals of image acquisition and computer vision.
Teaching methodology
Face-to-face lectures by the teacher on the topics of every chapter. Examples or use cases of every chapter will be given. Some concepts will be completed with some talks. Students will attend lab lessons, conducting their experiments, and will do their own homework to elaborate contents.
This subject requires face-to-face assistance of all students at the University of Santiago de Composteal to carry out part of their laboratory practices.
Evaluation system
Final exam- 40%
Homework and lab hand-outs- 60%
The final exam is a written exam hold face to face at every university with teacher and room provided by the coordinator at every university.
Studying time and personal work
This subject comprises 36 face-to-face lectures.
Face-to-face time with the teacher: classroom lectures, lab lectures and exam
Classroom lectures- 18 hours
Lab lectures- 20 hours
Exam- 4 hours
Overall- 42 hours
Students time
Classroom study time- 55 hours
Homework and lab student time- 65 hours
Overall- 120 hours
Overall subject time- 162 hours
Subject study recommendations
No secrets, just attend all the lectures, ask questions, probe further on either recommended bibliography or your own sources, Internet included, but above all, daily study, and office hours with the teachers.
Observations
Classrooms will be given in English