SGN-12007 Introduction to Image and Video Processing, 5 cr
This course is equivalent to SGN-12001
Sari Peltonen, Jenni Raitoharju, Moncef Gabbouj
|Accepted exercises, assignment and exam.|
After completing the course, the student is able to - explain the basics of human visual system, brightness adaptation and discrimination, image formation, image sampling and quantization, - present verbally or with mathematical formulas the spatial and frequency domain enhancement and restoration methods considered on the course for digital gray-scale images, - calculate outputs of the methods for simple images using the calculator, - explain the basics of color vision and pseudocolor images and the color models considered on the course, - explain the basic concepts of video processing, - implement covered operations independently for images using Matlab software.
|1.||Definition and representation of digital image, basics of the human visual system, brightness adaptation and discrimination, image formation, two-dimensional sampling and quantization||History of digital image processing, application areas and basic relationships between pixels|
|2.||Image enhancement and restoration both in spatial and frequency domains, two-dimensional discrete Fourier transform and classes of spatial and frequency domain filters||Continuous two-dimensional Fourier transform, properties of the two-dimensional discrete Fourier transform, other transforms and mathematical representation of individual filters||Mathematical derivation of the properties of the two-dimensional discrete Fourier transform|
|3.||The basics of color vision, color models and pseudocolor images||Color transformations between color models, color image smoothing and sharpening|
|4.||The basics of video processing, video file formats, resolutions and bit rates||Video enhancement|
|5.||Motion analysis and estimation, motion compensated filtering, deinterlacing and sampling rate conversion||MPEG standards|
|6.||Topics of the possible visiting lecture||Details of the possible visiting lecture|
Ohjeita opiskelijalle osaamisen tasojen saavuttamiseksi
The grade is determined by the exam (max 30 p.), exercises bonus (max 2 p.) and assignment bonus (max 2 p.). To pass the course the students has to have 8 out of 12 exercises accepted, assignment accepted and at to get least half of the exam maximum points. Exercises bonus (max 2 p.) and assignment bonus (max 2 p.) are valid only for the 3 exams of this implementation of the course. For the grade 3 the student should master the core content well. To obtain grade 4 student should also master some of the complementary knowledge. Student has the possibility to obtain grade 5 if he/she master well the complementary knowledge. If the students has minor lack of knowledge in core content he/she has possibility to obtain grade 1 or 2 depending on the amount of lacking knowledge. If there is serious lack of knowledge in core content, the student will not pass the course.
Numerical evaluation scale (0-5)
|Book||Digital Image Processing||Gonzalez, Woods||9780131687288||3rd edition, Prentice-Hall, New Jersey, 2008||No|
|Book||Practical Image and Video Processing Using MATLAB||Marques||9781118093467||No|
|Book||Video Processing and Communications||Wang, Ostermann, Zhang||130175471||No|
|Lecture slides||Moncef Gabbouj||Lecture notes shall be supplemented by materials from Chapters 1-6 of Digital Image Processing book.||Yes|
|SGN-11007 Introduction to Signal Processing||Mandatory|
Either SGN-11007 or equivalent knowledge of the basics of digital signal processing is required.
|SGN-12007 Introduction to Image and Video Processing, 5 cr||SGN-12006 Basic Course in Image and Video Processing, 5 cr|
|SGN-12007 Introduction to Image and Video Processing, 5 cr||SGN-12001 Introduction to Image and Video Processing, 5 cr|