Course detail

Advanced techniques of image processing

FEKT-LPZOAcad. year: 2010/2011

The theory and practice of advanced image processing techniques. Video sequence processing is included. The main areas of interest are camera model, its calibration, DFT image filtration, convolution, object recognition, biometric features recognition (skin, face, eye iris, papilar lines, walking), epipolar geometry, stereo pair analysis, correspondence problem, spatial information extraction, dynamic programming, homography, optical flow, motion in scene tracking.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

doc. Ing. Kamil Říha, Ph.D.

Department

Department of Telecommunications (UTKO)

Learning outcomes of the course unit

Students will be acquainted with image processing techniques and their implementation. Emphasis is laid on acquiring of practical experience with main algorithms implementation in C++ language and using OpenCV library.

Prerequisites

The subject knowledge on the degree level of master's prescribed courses is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

60 points written exam
40 points work and tests done in laboratory lessons

Course curriculum

1. OpenCV library, development environment setup, basic terms, rasterized image, gray level transformations
2. histogram, histogram equalization
3. geometric image transformations, homogenous coordinates
4. DFT, image filtration, convolution
5. edge, object detection in image
5. pinhole camera model, translation, rotation, calibration
6. biometric features recognition,skin , eye iris, face, papilar lines, walking
7. camera model, parallel projection, perspective projection, translation, rotation, calibration
8. epipolar geometry, image stereo pair
9. homography, panoramatic image assembling
10. spatial information extraction, correspondence problem, dynamic programming
11. optical flow, motion in scene tracking

Work placements

Not applicable.

Aims

To acquire applicative knowledge of advanced techniques of static and dynamic image processing 2D and 3D. Theoretical and practical mastering of advanced techniques image processing.

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

GONZALEZ, R. C., WOODS, R. E.: Digital Image Processing, Prentice Hall 2002, ISBN: 0201180758. (EN)
HLAVÁČ V., ŠONKA M.: Počítačové vidění, Grada, Praha 1992, ISBN 80-85424-67-3. (CS)
PARKER, J. R.: Algorithms for Image Processing and Computer Vision, Wiley; Bk&CD-Rom edition 1996, ISBN: 471140562. (EN)

Recommended reading

PRATA, S.: Mistrovství v C++, Computer Press, Brno 2004. (CS)

Classification of course in study plans

  • Programme EEKR-ML Master's

    branch ML-TIT , 2 year of study, summer semester, elective specialised

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Kamil Říha, Ph.D.

Syllabus

1. basic terms, rasterized image, histogram
2. OpenCV library, algorithmization of processing
3. geometric image transformations, homogenous coordinates
4. DFT, image filtration, convolution
5. edge, object detection in image
5. pinhole camera model, translation, rotation, calibration
6. biometric features recognition,skin , eye iris, face, papilar lines, walking
7. camera model, parallel projection, perspective projection, translation, rotation, calibration
8. epipolar geometry, image stereo pair
9. homography, panoramatic image assembling
10. spatial information extraction, correspondence problem, dynamic programming
11. optical flow, motion in scene tracking

Laboratory exercise

39 hod., compulsory

Teacher / Lecturer

doc. Ing. Kamil Říha, Ph.D.

Syllabus

1. acquainting with laboratory lessons content, OpenCV library introduction
2. practical acquainting with OpenCV library, inputs, outputs, processing
3. histogram, equalization, trasholding
4. geometrical transformation
5. camera calibration
6. DFT-2D
7. test
8. edge, object detection
9. epipolar geometry
10. homography, panoramatic image assembling
11. correspondence problem, dynamic programming
12. motion in scene tracking