456-0084/01 – Pattern Recognition and Computer Vision (AOPV)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantordoc. Dr. Ing. Eduard SojkaSubject version guarantordoc. Dr. Ing. Eduard Sojka
Study levelundergraduate or graduateRequirementChoice-compulsory
Year4Semestersummer
Study languageCzech
Year of introduction1992/1993Year of cancellation2002/2003
Intended for the facultiesFEIIntended for study typesMaster
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2

Subject aims expressed by acquired skills and competences

The subjet acquaints the students with the fundamental methods of image anlysis.

Teaching methods

Summary

The following topics are discussed: Image segmentation. Detecting edges, regions, corners. Measuring objects. Pattern recognition based on classification. Statistical classification. Bayes' risk. Using neural networks for pattern recognition. Processing and analysis of images of 3D scenes. Processing images varying in time. Tracking objects.

Compulsory literature:

Recommended literature:

Way of continuous check of knowledge in the course of semester

Conditions for credit: The tasks assigned during the exercises must be worked out.

E-learning

Další požadavky na studenta

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: Detecting edges in images. Gradient, and zero-crossing methods. Parametric edge models. Canny edge detector. Thresholding. Optimal threshold selection. Image segmentaion based on region growing and splitting. Edge linking. Heuristic edge following. Method of clustering in the space of parameters. Representing boundaries and areas: Encoding boundaries using line segments and curves. Line and curve fitting. Representing areas. Detecting feature points (corners). Measuring objects. Selection and computation of features for pattern recognition. Evaluating the efficiency and optimisation of the set of selected features. Pattern recognition based on classification. Discriminant functions and etalons. Probablistic approach to determining the discriminant functions. Using neural networks for pattern recognition. Reconstructing a scene from its two or more images. Absolute and relative camera calibration and reconstruction. Analysis of time-varying images. Tracking objects in image sequences. Computer labs: During the exercises, the students work out a series of practical tasks (detecting edges, areas, corners, computing features, etalon-based classification). The tasks are prepared in the form of templates (pre-prepared programs) into which the sudents fill their own source code. In this way, they can focus on substantial and interesting issues. Furthermore, examples of reconstructing 3D scenes are presented.

Conditions for subject completion

Full-time form (validity from: 1960/1961 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Exercises evaluation and Examination Credit and Examination 100 (145) 51
        Examination Examination 100  0
        Exercises evaluation Credit 45  0
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2002/2003 (M2612) Electrical Engineering and Computer Science (2601T004) Measurement and Control Engineering P Czech Ostrava Choice-compulsory study plan
2002/2003 (M2612) Electrical Engineering and Computer Science (2612T018) Electronics and Communication Technology P Czech Ostrava Choice-compulsory study plan
2002/2003 (M2612) Electrical Engineering and Computer Science (2642T004) Electrical Machines, Apparatus and Drives (10) Elektrické stroje a přístroje P Czech Ostrava Choice-compulsory study plan
2002/2003 (M2612) Electrical Engineering and Computer Science (2642T004) Electrical Machines, Apparatus and Drives (20) Elektrické pohony a výkonová elektronika P Czech Ostrava Choice-compulsory study plan
2002/2003 (M2612) Electrical Engineering and Computer Science (3902T023) Computer Science P Czech Ostrava Choice-compulsory study plan
2002/2003 (M2612) Electrical Engineering and Computer Science (3907T001) Electrical Power Engineering P Czech Ostrava Choice-compulsory study plan
2002/2003 (M2612) Electrical Engineering and Computer Science (3902T023) Computer Science P Czech Ostrava 4 Choice-compulsory study plan
2001/2002 (M2612) Electrical Engineering and Computer Science (2601T004) Measurement and Control Engineering P Czech Ostrava Choice-compulsory study plan
2001/2002 (M2612) Electrical Engineering and Computer Science (2612T018) Electronics and Communication Technology P Czech Ostrava Choice-compulsory study plan
2001/2002 (M2612) Electrical Engineering and Computer Science (2642T004) Electrical Machines, Apparatus and Drives (10) Elektrické stroje a přístroje P Czech Ostrava Choice-compulsory study plan
2001/2002 (M2612) Electrical Engineering and Computer Science (2642T004) Electrical Machines, Apparatus and Drives (20) Elektrické pohony a výkonová elektronika P Czech Ostrava Choice-compulsory study plan
2001/2002 (M2612) Electrical Engineering and Computer Science (3902T023) Computer Science P Czech Ostrava Choice-compulsory study plan
2001/2002 (M2612) Electrical Engineering and Computer Science (3907T001) Electrical Power Engineering P Czech Ostrava Choice-compulsory study plan
2001/2002 (M2612) Electrical Engineering and Computer Science (3902T023) Computer Science P Czech Ostrava 4 Choice-compulsory study plan
2000/2001 (M2612) Electrical Engineering and Computer Science (3902T023) Computer Science P Czech Ostrava 4 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner