Real time machine vision

Code:

MVCOMP12

Acronym:

VATR

Keywords
Classification	Keyword
CNAEF	Engineering and related techniques

Instance: 2024/2025 - 3T

Active?	Yes
Responsible unit:	Department of Electrical and Computer 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	0	Syllabus	1	-	3	21	81

Teaching language

English
Obs.: Lecionada por docente da Universidade de Vigo

Objectives

The students will learn how to efficiently program real time acquisition and processing of images for machine vision applications.

Learning outcomes and competences

By exposing and exploring the different concepts of real time machine vision we expect that the student will be able to distinguish and apply these concepts correctly, is able to analyze and synthetize knowledge; by analyzing the main learning paradigms for real time machine vision, we expect that the student will be able to understand them; and by studying relevant and existing real time machine vision applications, we expect the student will more easily obtain competences for developing these applications, together with the application development projects that the student
will have to do in this course.
Upon the successful conclusion of the course, students should have the ability to work in team,
organization and planning; the ability to analyze and synthesize knowledge; the ability to develop real time machine vision systems depending on existing needs and apply the most appropriate technological tools; the knowledge of the fundamentals of real time machine vision and its applications; and acquire the learning skills that allow to continue studying in a way that will be largely self-directed or autonomous.

Working method

À distância

Program

Real time pr ogramming for machine vision.
PC-frame-grabber communication.
Memory management.
Structure and usage of a typical machine vision SDK.
Low-level programming for high speed industrial processes.

Mandatory literature

Samuel P. Harbison, Guy L. Steele Jr.; C: A Reference Manual (5th Edition), Pearson, 2002

Comments from the literature

Other titles to be listed.

Teaching methods and learning activities

Participatory classes, laboratory practice with cameras and computer equipment, project-oriented learning and resolution of practical use cases, autonomous work and independent study by the student, group work and group work and cooperative learning.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

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

Amount of time allocated to each course unit

Designation	Time (hours)
Apresentação/discussão de um trabalho científico	10,00
Elaboração de projeto	30,00
Estudo autónomo	20,00
Frequência das aulas	18,00
Trabalho laboratorial	3,00
Total:	81,00

Eligibility for exams

To be defined.

Calculation formula of final grade

The student's evaluation (100%) will be based entirely on the student's work and
student work and results obtained during the trimester.