Course detail

Recognition

FEKT-MROZAcad. year: 2019/2020

The course Recognition engages in methods of objects segmentation, detection and description of interest points and regions, classification and categorization, learning in recognition and multiimage reconstruction.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

Ing. Karel Horák, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

Graduate of the course is able to analyse a recognition task of computer vision, to design and implement solution of it and to perform meaningful verification of a result.

Prerequisites

The knowledge on the level of the Bachelor's degree is required in the Recognition course. Moreover, knowledge and skills equivalent to the MPOV / LPOV course are required.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods include lectures and a group project. Course is taking advantage of the e-learning (Midas) system.

Assesment methods and criteria linked to learning outcomes

A group project (40 pts) and a final exam (60 pts) are evaluated during the Recognition course. For successful pass the course, obtaining of at least half of available points is required in both mentioned parts.

Course curriculum

1. Application in computer vision.
2. Cluster-based segmentation.
3. Local features and correspondences.
4. Region detector.
5. Region descriptors.
6. Global and combined descriptors.
7. Image understanding.
8. Distance and risk minimization classification.
9. Dynamic images.
10. Multiimage reconstruction.
11. Special application in computer vision.
12. Learning in recognition.
13. Selected passages of recognition.

Work placements

Not applicable.

Aims

Aim of the course is to acquaint students with a term of object recognition based on local invariant descriptors and by means of learning in recognition. Students design, implement and verify task of object recognition in computer vision domain during a group projects.

Specification of controlled education, way of implementation and compensation for absences

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

DUDA, R.O., HART, P.E., STORK, D.G.: Pattern Classification. 2nd edition. Wiley, 2000. 680 pages. ISBN 978-0471056690. (EN)
HARTLEY, R., ZISSERMAN, A.: Multiple View Geometry in Computer Vision. 2nd edition. Cambridge University Press, 2004. 670 pages. ISBN 978-0521540513. (EN)
SZELISKI, R.: Computer Vision: Algorithms and Applications. Springer, 2011. 812 pages. ISBN 978-1848829343. (EN)

Classification of course in study plans

  • Programme EEKR-M Master's

    branch M-KAM , 1 year of study, summer semester, elective specialised

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, summer semester, elective specialised

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Karel Horák, Ph.D.

Laboratory exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Karel Horák, Ph.D.
Ing. Ondřej Boštík