Digital image processing
Course description sheet
Basic information
- Field of study
- Geospatial Computer Science
- Major
- -
- Organisational unit
- Faculty of Geo-Data Science, Geodesy, and Environmental Engineering
- Study level
- First-cycle (engineer) programme
- Form of study
- Full-time studies
- Profile
- General academic
- Didactic cycle
- 2022/2023
- Course code
- DGEIS.Ii20.06398.22
- Lecture languages
- English
- Mandatoriness
- Elective
- Block
- Elective Modules in Foreign Language
- Course related to scientific research
- Yes
Lecturer
Sławomir Mikrut
|
Period
Semester 6
|
Method of verification of the learning outcomes
Completing the classes
Activities and hours
Lectures:
15
Project classes: 15 |
Number of ECTS credits
3
|
Course's learning outcomes
| Code | Outcomes in terms of | Learning outcomes prescribed to a field of study | Methods of verification |
| Knowledge – Student knows and understands: | |||
| W1 | the methods of compression and filtration of digital images, process automation | GEI1A_W03, GEI1A_W04, GEI1A_W05 | Test |
| W2 | a synthetic knowledge of applied digital image processing methods | GEI1A_W01, GEI1A_W02, GEI1A_W03 | Test |
| Skills – Student can: | |||
| U1 | perform basic operations on digital images including: digital image filtering with appropriate algorithms and lossy and lossless compression; can automate processes | GEI1A_U03, GEI1A_U05 | Execution of laboratory classes, Test |
| Social competences – Student is ready to: | |||
| K1 | do independently and be creative in solving problems | GEI1A_K01, GEI1A_K02, GEI1A_K03 | Test |
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Lectures | 15 | |
| Project classes | 15 | |
| Preparation for classes | 15 | |
| Realization of independently performed tasks | 15 | |
| Examination or final test/colloquium | 2 | |
| Preparation of project, presentation, essay, report | 15 | |
| Student workload |
Hours
77
|
|
| Workload involving teacher |
Hours
30
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. | Basic definitions in digital image processing: Basic definitions used in digital image processing, examples of simple images used in geodesy and cartography, multi-dimensional images. Decimal and binary system (computer image recording). | W1, W2, K1 | Lectures |
| 2. | Basic operations on digital images: The aim of the course is to familiarize students with basic operations in digital images such as compression or filtration. | W1, W2, U1, K1 | Project classes |
| 3. | Filtration of digital images: The aim of the course is to familiarize students with basic operations on digital images in Matlab. Software algorithms for filtering images and feature extraction. | W1, W2, U1, K1 | Project classes |
| 4. | Methods of obtaining digital images: Digital image acquisition methods: direct, indirect. Sampling, quantization Geometric, spectral, radiometric, color spaces. Compression lossless and lossy digital images. | W1, W2, K1 | Lectures |
| 5. | Compression and filtering of digital images: Lossless and lossy compression methods of digital images. Split filters. Linear and nonlinear filters. Conflict. Upper and lower pass filters. Edge extraction. Filtration in the frequency space. Removing noise from the image. Texture and structure of the image. | W1, W2, K1 | Lectures |
| 6. | Automation of processes: The aim of the course is to familiarize students with the basic operations of digital images related to process automation in Matlab. Executing an example project that automates the process. | W1, W2, U1, K1 | Project classes |
| 7. | Geometric processing of images: Geometric processing of images: rotation, scale change, surface projection. Transformations: Helmert, affine, projective, polynomial, finite elements. Re-sampling Image pyramids. Matching images. Application of image analysis algorithms. | W1, W2, K1 | Lectures |
| 8. | Digital image processing - practical examples: Image processing in sample projects. Actions on multispectral images. Main component method. Linking images with different geometric resolutions. Pansharpening Technique - Examples. Steganography. | W1, W2, K1 | Lectures |
Extended information/Additional elements
Teaching methods and techniques :
Lectures
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Lectures | Test | |
| Project classes | Execution of laboratory classes, Test |
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Lectures: Studenci uczestniczą w zajęciach poznając kolejne treści nauczania zgodnie z syllabusem przedmiotu. Studenci winni na bieżąco zadawać pytania i wyjaśniać wątpliwości. Rejestracja audiowizualna wykładu wymaga zgody prowadzącego. Project classes: Studenci wykonują prace praktyczne mające na celu uzyskanie kompetencji zakładanych przez syllabus. Ocenie podlega sposób wykonania projektu oraz efekt końcowy.
Literature
Obligatory- 1. Gonzales R.C., Woods R.E., 2008, Digital Image Processing, Third Edition. Pearson Education, Inc.
- 2. Bishop C.M., 2006, Pattern Recognition and Machine Learning, Springer.
- 3. Malina W., Siemiatacz M.,2005, Metody cyfrowego przetwarzania obrazów. EXIT.
- 4. Skarbek W., 1993, Metody reprezentacji obrazów cyfrowych. Akademicka Oficyna
- Wydawnicza PLJ, Warszawa.
- 5. Tadeusiewicz R., Korohoda P., 1997, Komputerowa analiza obrazów. Wydawnictwo Fundacji
- Postępu Komunikacji, Kraków.
Scientific research and publications
Publications- 1. Czechowicz A., , Gryboś P., Jachimski J., Mikrut S., Mikrut Z. , Pawlik P., Tadeusiewicz R. (Redakcja naukowa dr inż. Sławomir Mikrut). Sieci neuronowe w procesach dopasowania zdjęć lotniczych. Autorzy: Wydawnictwa AGH. Kraków. 2010.
- 2. Mikrut S., Mikrut Z., Moskal A. ,Pastucha E. : Detection and Recognition of Selected Class Railway signs. Image Processing & Communications : an International Journal; ISSN 1425-140X, 2014 vol. 19 no. 2–3, s. 83–96, 2014
- 3. Mikrut, 2012. Integration of Digital Images and laser scanning data based on selected images. Automatyka. Image Processing & Communications : an International Journal ; ISSN 1425-140X. vol. 17 no. 4, s. 161–166, 2012.
- 4. Mikrut S., Mikrut Z., Neural networks in the automation of photogrammetric processes. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing, s. 331-336. 2008.