|OFICIAL||Interaction and Multimedia|
|Responsible unit:||Department of Informatics Engineering|
|Course/CS Responsible:||Master in Informatics and Computing Engineering|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|MIEIC||42||Syllabus since 2009/2010||5||-||6||42||162|
|Luís Filipe Pinto de Almeida Teixeira|
|Jorge Alves da Silva|
Computer vision is a subfield of computer science that focuses on extracting "useful information" from images and videos. The goal of computer vision is to "discover from images what is present in the world, where things are located, what actions are taking place" (Marr, 1982). Examples of "useful information" include, for example, recovering the 3D geometry of objects in an image, detection and identification of human faces and gestures, and tracking moving people or vehicles in a video sequence. Computer vision algorithms have found a wide range of applications in the industrial, military and medical fields, as well as in the ever-growing entertainment field.
This course is an introduction to basic concepts and methods in computer vision. It is mainly suited for MIEIC students who are interested in following research in this area. The covered topics include: image formation, basic image processing and analysis methods, as well as more advanced methods like 3D scene reconstruction, motion analysis, tracking, and object recognition.
Upon completion of this course, students will:
Approval in the Programming, Algorithms and Data Stuctures, and Algebra courses (or equivalent) is advisable.
Introduction to Computer Vision
Processing and analysis of intensity images
Geometric calibration of a camera
Motion and tracking
• Lectures: Presentation and discussion of the course topics, and resolution of exercises.
• Practical assignments: Development of projects where the studied computer vision methods must be applied.
|Frequência das aulas||42,00|
Do not exceed the absence limit and obtain a minimum of 40% in the distributed evaluation classification.
Distributed evaluation (DEv) with final exam (ExEv).
DEv - Two projects will be developed during the semester; these projects must be developed both during classes and at home. For the last project a report will be written and an oral presentation will be required. • DEv and ExEv are specified in a 0 to 20 scale.
Projects weight in DEv:
• Final Classification = DEv * 0.5 + ExEv * 0.5
Oral examination: whenever needed, according to a decision of the teaching team, students may be submitted to an oral exam. In this situation the final classification will be given by the average of the classification calculated with the previous formula and the classification of the oral exam.
Observations: 1- A minimum of 40% on the ExEv evaluation component is required to be approved in the course. 2- If the teaching team decides not to propose one of the projects, its weight will be redistributed to the other projects.
See DEv, in Evaluation components.
Students with a special status will be assessed in the same way as ordinary students. They have to do all the assignments and deliver them on the scheduled dates.
Students can only improve the mark of the distributed evaluation component in the following year. Students can improve the mark of the written exam at the corresponding seasons (according to the rules).