PA171 Integral and Discrete Transforms in Image Processing

Faculty of Informatics
Autumn 2023
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. David Svoboda, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Wed 10:00–11:50 B411
  • Timetable of Seminar Groups:
PA171/01: Fri 8:00–9:50 B311, D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 50 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations of changing the image content or transforming the original data into a different representation. At the end of this course, students should be able to:
- understand the basic principles of the image transforms;
- know the selected transforms;
- implement and apply the selected transforms;
- understand standard image compression algorithms;
- correctly resample images;
- use suitable image restoration algorithms.
Learning outcomes
After completing the course, the student should be able to:
- analyze the image data in a frequency domain;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately;
- solve the tasks focused on image restoration;
- appropriately use the resampling algorithms and understand their results
Syllabus
  • Discrete transforms (Fourier transform, Hadamard, PCA, DCT, Wavelets)
  • Optimized discrete transforms (FFT, F-DCT, FWT, Lifting scheme)
  • Image compression, Lossy/Lossless compression
  • Compression standards (JPEG, JPEG2000, H.265)
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Image restoration
  • Steerable filters
Literature
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During the semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of the semester. The students must successfully pass this defense in order to be allowed to take the final exam. The final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have a chance to explain or finalize those solutions from the written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught last offered.
Teacher's information
https://cbia.fi.muni.cz/education/
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022.

PA171 Integral and Discrete Transforms in Image Processing

Faculty of Informatics
Autumn 2022
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. David Svoboda, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Mon 8:00–9:50 B204
  • Timetable of Seminar Groups:
PA171/01: Wed 8:00–9:50 B311, D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 50 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations of changing the image content or transforming the original data into a different representation. At the end of this course, students should be able to:
- understand the basic principles of the image transforms;
- know the selected transforms;
- implement and apply the selected transforms;
- understand standard image compression algorithms;
- correctly resample images;
- use suitable image restoration algorithms.
Learning outcomes
After completing the course, the student should be able to:
- analyze the image data in a frequency domain;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately;
- solve the tasks focused on image restoration;
- appropriately use the resampling algorithms and understand their results
Syllabus
  • Discrete transforms (Fourier transform, Hadamard, PCA, DCT, Wavelets)
  • Optimized discrete transforms (FFT, F-DCT, FWT, Lifting scheme)
  • Image compression, Lossy/Lossless compression
  • Compression standards (JPEG, JPEG2000, H.265)
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Image restoration
  • Steerable filters
Literature
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During the semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of the semester. The students must successfully pass this defense in order to be allowed to take the final exam. The final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have a chance to explain or finalize those solutions from the written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
https://cbia.fi.muni.cz/education/
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Autumn 2021
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. David Svoboda, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Mon 13. 9. to Mon 6. 12. Mon 10:00–11:50 B410
  • Timetable of Seminar Groups:
PA171/01: Mon 13. 9. to Mon 6. 12. Mon 14:00–15:50 B311, D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 49 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations of changing the image content or transforming the original data into a different representation. At the end of this course, students should be able to:
- understand the basic principles of the image transforms;
- know the selected transforms;
- implement and apply the selected transforms;
- understand standard image compression algorithms;
- correctly resample images;
- use suitable image restoration algorithms.
Learning outcomes
After completing the course, the student should be able to:
- analyze the image data in a frequency domain;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately;
- solve the tasks focused on image restoration;
- appropriately use the resampling algorithms and understand their results
Syllabus
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
Literature
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During the semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of the semester. The students must successfully pass this defense in order to be allowed to take the final exam. The final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have a chance to explain or finalize those solutions from the written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
https://cbia.fi.muni.cz/education/
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Autumn 2019
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. David Svoboda, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Mon 10:00–11:50 A218
  • Timetable of Seminar Groups:
PA171/01: Tue 8:00–9:50 B311, D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 49 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations of changing the image content or transforming the original data into a different representation. At the end of this course, students should be able to:
- understand the basic principles of the image transforms;
- know the selected transforms;
- implement and apply the selected transforms;
- understand standard image compression algorithms;
- correctly resample images;
- use suitable image restoration algorithms.
Learning outcomes
After completing the course, the student should be able to:
- analyze the image data in a frequency domain;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately;
- solve the tasks focused on image restoration;
- appropriately use the resampling algorithms and understand their results
Syllabus
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
Literature
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During the semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of the semester. The students must successfully pass this defense in order to be allowed to take the final exam. The final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have a chance to explain or finalize those solutions from the written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2018
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Tue 16:00–17:50 C416
  • Timetable of Seminar Groups:
PA171/01: Wed 8:00–9:50 B311, D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 20 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to:
- understand the basic principles of the image transforms;
- know the selected transforms;
- implement and apply the selected transforms;
- understand standard image compression algorithms;
- correctly resample images;
- use suitable image restoration algorithms.
Learning outcomes
After completing the course, the student should be able to:
- analyze the image data in frequency domain;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately;
- solve the tasks focused on image restoration;
- appropriately use the resampling algorithms and understand their results
Syllabus
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
Literature
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of semester. The students must successfully pass this defense in order to be allowed to take the final exam. Final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have chance to explain or finalize those solutions from written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2017
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Tue 8:00–9:50 B411
  • Timetable of Seminar Groups:
PA171/01: Fri 8:00–9:50 B311, D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 20 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement and apply the selected transforms; understand standard image compression algorithms; correctly resample images; use suitable image restoration algorithms.
Syllabus
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of semester. The students must successfully pass this defense in order to be allowed to take the final exam. Final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have chance to explain or finalize those solutions from written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2016
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Mon 8:00–9:50 B204
  • Timetable of Seminar Groups:
PA171/01: Wed 12:00–13:50 B311, D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 20 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement and apply the selected transforms; understand standard image compression algorithms; correctly resample images; use suitable image restoration algorithms.
Syllabus
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of semester. The students must successfully pass this defense in order to be allowed to take the final exam. Final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have chance to explain or finalize those solutions from written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2015
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Wed 8:00–9:50 B411, Fri 8:00–9:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 19 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement and apply the selected transforms; understand standard image compression algorithms; correctly resample images; use suitable image restoration algorithms.
Syllabus
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of semester. The students must successfully pass this defense in order to be allowed to take the final exam. Final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have chance to explain or finalize those solutions from written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2014
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Tue 10:00–11:50 C416, Wed 8:00–9:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 19 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement and apply the selected transforms; understand standard image compression algorithms; correctly resample images; use suitable image restoration algorithms.
Syllabus
  • Thresholding (various methods of histogram analysis)
  • Linear and nonlinear filtering
  • Edge detection (Canny, Deriche, etc.)
  • Discrete transforms (Fourier, FFT, Hough, Hadamard, Discrete Cosine, Wavelets, Radon, etc.)
  • Recursive filtering
  • Deconvolution
  • Image compression, loss/lossless compression, colour indexing, entropy, JPEG, MPEG, the use in image formats
  • Texture filtering
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of semester. The students must successfully pass this defense in order to be allowed to take the final exam. Final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have chance to explain or finalize those solutions from written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2013
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Tue 8:00–9:50 C416, Tue 10:00–11:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 19 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement the selected transforms; apply the selected transforms.
Syllabus
  • Thresholding (various methods of histogram analysis)
  • Linear and nonlinear filtering
  • Edge detection (Canny, Deriche, etc.)
  • Discrete transforms (Fourier, FFT, Hough, Hadamard, Discrete Cosine, Wavelets, Radon, etc.)
  • Recursive filtering
  • Deconvolution
  • Image compression, loss/lossless compression, colour indexing, entropy, JPEG, MPEG, the use in image formats
  • Texture filtering
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
Lectures in Czech (optionally in English), study materials in English. Exercises in computer labs. Final exam in written and oral form.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2012
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Mon 10:00–11:50 B204, Mon 12:00–13:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 19 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement the selected transforms; apply the selected transforms.
Syllabus
  • Thresholding (various methods of histogram analysis)
  • Linear and nonlinear filtering
  • Edge detection (Canny, Deriche, etc.)
  • Discrete transforms (Fourier, FFT, Hough, Hadamard, Discrete Cosine, Wavelets, Radon, etc.)
  • Recursive filtering
  • Deconvolution
  • Image compression, loss/lossless compression, colour indexing, entropy, JPEG, MPEG, the use in image formats
  • Texture filtering
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
Lectures in Czech (optionally in English), study materials in English. Exercises in computer labs. Final exam in written and oral form.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2011
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Timetable
Wed 10:00–11:50 C416, Wed 16:00–16:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 21 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement the selected transforms; apply the selected transforms.
Syllabus
  • Thresholding (various methods of histogram analysis)
  • Linear and nonlinear filtering
  • Edge detection (Canny, Deriche, etc.)
  • Discrete transforms (Fourier, FFT, Hough, Hadamard, Discrete Cosine, Wavelets, Radon, etc.)
  • Deconvolution
  • Image compression, loss/lossless compression, colour indexing, entropy, JPEG, MPEG, the use in image formats
  • Texture filtering
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
Lectures in Czech (optionally in English), study materials in English. Exercises in computer labs. Final exam in written and oral form.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2010
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Timetable
Mon 12:00–13:50 C416, Mon 16:00–16:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 17 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement the selected transforms; apply the selected transforms.
Syllabus
  • Thresholding (various methods of histogram analysis)
  • Linear and nonlinear filtering
  • Edge detection (Canny, Deriche, etc.)
  • Discrete transforms (Fourier, FFT, Hough, Hadamard, Discrete Cosine, Wavelets, Radon, etc.)
  • Deconvolution
  • Image compression, loss/lossless compression, colour indexing, entropy, JPEG, MPEG, the use in image formats
  • Texture filtering
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
Lectures in Czech (optionally in English), study materials in English. Exercises in computer labs. Final exam in written and oral form.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2009
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Timetable
Mon 8:00–9:50 B411, Mon 10:00–10:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 14 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to: understand the basic principles of the image transforms; know the selected transforms; implement the selected transforms; apply the selected transforms.
Syllabus
  • Thresholding (various methods of histogram analysis)
  • Linear and nonlinear filtering
  • Edge detection (Canny, Deriche, etc.)
  • Discrete transforms (Fourier, FFT, Hough, Hadamard, Discrete Cosine, Wavelets, Radon, etc.)
  • Deconvolution
  • Image compression, loss/lossless compression, colour indexing, entropy, JPEG, MPEG, the use in image formats
  • Texture filtering
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Assessment methods
Lectures in Czech (optionally in English), study materials in English. Exercises in computer labs. Final exam in written and oral form.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2008
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Timetable
Tue 12:00–13:50 C416, Tue 15:00–15:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 14 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. The use of all these transforms will be shown as well.
Syllabus
  • Thresholding (various methods of histogram analysis)
  • Linear and nonlinear filtering
  • Edge detection (Canny, Deriche, etc.)
  • Discrete transforms (Fourier, FFT, Hough, Hadamard, Discrete Cosine, Wavelets, Radon, etc.)
  • Deconvolution
  • Image compression, loss/lossless compression, colour indexing, entropy, JPEG, MPEG, the use in image formats
  • Texture filtering
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Assessment methods (in Czech)
Přednášky v češtině, studijní materiály v angličtině. Cvičení u počítačů. Závěrečná zkouška v písemné i ústní podobě.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Filtering

Faculty of Informatics
Spring 2007
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Timetable
Thu 8:00–9:50 C416, Thu 11:00–11:50 B117
  • Timetable of Seminar Groups:
PA171/01: No timetable has been entered into IS. D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. The use of all these transforms will be shown as well.
Syllabus
  • Thresholding (various methods of histogram analysis)
  • Linear and nonlinear filtering
  • Edge detection (Canny, Deriche, etc.)
  • Discrete transforms (Fourier, FFT, Hough, Hadamard, Discrete Cosine, Wavelets, Radon, etc.)
  • Deconvolution
  • Image compression, loss/lossless compression, colour indexing, entropy, JPEG, MPEG, the use in image formats
  • Texture filtering
Literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
  • PRATT, William K. Digital image processing. 3rd ed. New York: John Wiley & Sons, 2001, xix, 735. ISBN 0471374075. info
Assessment methods (in Czech)
Přednášky v češtině, studijní materiály v angličtině. Cvičení u počítačů. Závěrečná zkouška v písemné i ústní podobě.
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Autumn 2020

The course is not taught in Autumn 2020

Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught online.
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. David Svoboda, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable of Seminar Groups
PA171/01: No timetable has been entered into IS. D. Svoboda
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 49 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations of changing the image content or transforming the original data into a different representation. At the end of this course, students should be able to:
- understand the basic principles of the image transforms;
- know the selected transforms;
- implement and apply the selected transforms;
- understand standard image compression algorithms;
- correctly resample images;
- use suitable image restoration algorithms.
Learning outcomes
After completing the course, the student should be able to:
- analyze the image data in a frequency domain;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately;
- solve the tasks focused on image restoration;
- appropriately use the resampling algorithms and understand their results
Syllabus
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
Literature
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During the semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of the semester. The students must successfully pass this defense in order to be allowed to take the final exam. The final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have a chance to explain or finalize those solutions from the written part that are incomplete.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
https://cbia.fi.muni.cz/education/
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.

PA171 Digital Image Filtering

Faculty of Informatics
Spring 2019

The course is not taught in Spring 2019

Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. David Svoboda, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. David Svoboda, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Prerequisites
PV131 Digital Image Processing
Knowledge of written English and calculus is required.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 20 fields of study the course is directly associated with, display
Course objectives
The aim of this lecture is to introduce all the basic image transforms used in digital image processing. It covers the operations changing the image content or transforming the original data into different representation. At the end of this course, students should be able to:
- understand the basic principles of the image transforms;
- know the selected transforms;
- implement and apply the selected transforms;
- understand standard image compression algorithms;
- correctly resample images;
- use suitable image restoration algorithms.
Learning outcomes
After completing the course, the student should be able to:
- analyze the image data in frequency domain;
- discuss the problems in the field of frequency analysis;
- propose her/his own efficient and optimized compression methods;
- demonstrate the general principles of compression algorithms;
- use wavelet and Fourier transform appropriately;
- solve the tasks focused on image restoration;
- appropriately use the resampling algorithms and understand their results
Syllabus
  • Discrete transforms (Fourier transform, FFT, Hadamard, DCT, Wavelets)
  • Image compression, Lossy/Lossless compression, JPEG, JPEG2000, MPEG
  • Sampling, Resampling, Signal reconstruction, Texture filtering
  • Z-transform, Recursive filtering
  • Deconvolution
  • Edge detection (Canny, Deriche, etc.)
  • Image descriptors (Haralick, Zernike, SIFT, MPEG-7)
  • Steerable filters
Literature
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing [2nd ed.]. 2nd ed. Upper Saddle River: Prentice Hall, 2002, xx, 793 s. ISBN 0-201-18075-8. info
  • BRACEWELL, Ronald N. The Fourier transform and its applications. 3rd ed. Boston: McGraw Hill, 2000, xx, 616. ISBN 0073039381. URL info
Teaching methods
obtaining knowledge during lectures, obtaining skills by working with PC
Assessment methods
During semester, the students are required to solve the selected team project. The final defense of this project takes place during the last week of semester. The students must successfully pass this defense in order to be allowed to take the final exam. Final exam consists of written and oral form. The written part contains questions that verify the students' skills and experience in the given field of image processing. The oral part follows the written part. Here, the students have chance to explain or finalize those solutions from written part that are incomplete.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
http://cbia.fi.muni.cz/teaching-activities.html
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Autumn 2019, Autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)