Instrumentation and processing for machine vision

Code:

MVCOMP05

Acronym:

IPVA

Keywords
Classification	Keyword
CNAEF	Engineering and related techniques

Instance: 2022/2023 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Computer Vision

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MVCOMP	3	Syllabus	1	-	6	42	162

Teaching language

English

Objectives

This curricular unit goes through the aspects of image acquisition and image processing. From the viewpoint of the industrial application the course also elucidates in topics like illumination and camera calibration. Attention is paid to the hardware aspects, starting from lenses and camera systems to camera-computer interfaces. Besides the hardware analysis, the necessary software is discussed with equal depth. This includes connections to digital image basics as well as image analysis and processing (discussed in other curricular units). Finally, the student is introduced to general aspects of industrial applications of machine vision, such as case studies and strategies for the conception of complete machine vision systems. The student will be enabled not only to understand up to date systems for machine vision but will also be qualified for the planning and evaluation of such technology.

Learning outcomes and competences

Upon completion of this course, students should be able:

- to identify the main hardware components of a machine vision system and understand their characteristics;

– to select the fundamental components of a machine vision system, taking into account a set of specifications;

– to select and apply common computer vision algorithms, provided through a library like OpenCV, to solve medium complexity problems, envolving the acquisition of 2D or 3D images;

– to analyze and understand selected scientific papers in image processing and analysis, and computer vision.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Knowledge of a programming language, preferably Python or C++, and basic algebra concepts are recommended.

Program

– Image and video acquisition: lighting, lenses, sensors and interfaces.

– Smart image sensors.

– Machine vision algorithms.

– Geometric camera calibration.

– 3D data acquisition.

– Industrial Machine Vision Systems and applications.

Mandatory literature

Alexander Hornberg; Handbook of Machine and Computer Vision: The Guide for Developers and Users
E. R. Davies; Machine Vision, Theory, Algorithms, Practicalities
Adrian Kaehler; Learning OpenCV 3. ISBN: 978-1-491-93799-0
Laurent Berger; Traitement d'images et de vidéos avec OpenCV 4 en Python (Windows, Linux, Raspberry)

Complementary Bibliography

Junichi Nakamura; Image Sensors & Signal Processing for Digital Still Cameras
Jun Ohta; Smart Image Sensors and Applications
Richard Szeliski; Computer vision. ISBN: 978-1-84882-935-0
Rafael C. González, Richard E. Woods; Digital Image Processing, 2007

Teaching methods and learning activities

– 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 complemented with some talks by third parties (e.g. hardware/software sellers)

– Students will attend lab lessons, conducting their experiments, and will do their own homework to elaborate contents.

Software

OpenCV library

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	40,00
Trabalho prático ou de projeto	60,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	43,00
Estudo autónomo	42,00
Frequência das aulas	39,00
Trabalho escrito	30,00
Trabalho laboratorial	8,00
Total:	162,00

Eligibility for exams

Submitting and presenting the practical assignments is mandatory.

Calculation formula of final grade

Final_grade = Exam_grade*40% + Distributed_grade*60%

Special assessment (TE, DA, ...)

Students with a special status will be assessed in the same way as ordinary students: they have to do all the assignments and lab work, during the semester, submitting them on the scheduled dates.

Classification improvement

The grade of the final exam can be improved in the next exam seasons, according to FEUP rules. The distributed evaluation grade is the one obtained at the end of classes and it can only be improved in a future occurrence of this course.