Szkoła Doktorska - ZUT Doctoral School
specjalność: IT, ELECTRICAL ENGINEERING AND MECHANICAL ENGINEERING BLOCK
Sylabus przedmiotu Computer vision:
Informacje podstawowe
| Kierunek studiów | ZUT Doctoral School | ||
|---|---|---|---|
| Forma studiów | studia stacjonarne | Poziom | |
| Stopnień naukowy absolwenta | doktor | ||
| Obszary studiów | charakterystyki PRK | ||
| Profil | |||
| Moduł | — | ||
| Przedmiot | Computer vision | ||
| Specjalność | IT, ELECTRICAL ENGINEERING AND MECHANICAL ENGINEERING BLOCK | ||
| Jednostka prowadząca | Katedra Systemów Multimedialnych | ||
| Nauczyciel odpowiedzialny | Paweł Forczmański <Pawel.Forczmanski@zut.edu.pl> | ||
| Inni nauczyciele | |||
| ECTS (planowane) | 0,5 | ECTS (formy) | 0,5 |
| Forma zaliczenia | zaliczenie | Język | angielski |
| Blok obieralny | 9 | Grupa obieralna | 1 |
Wymagania wstępne
| KOD | Wymaganie wstępne |
|---|---|
| W-1 | Fundamentals of linear algebra |
| W-2 | Fundamentals of probability calculus |
| W-3 | Fundamentals of algorithmics and numerical methods |
| W-4 | practical knowledge of the selected programming language: C/C++, Python, Matlab |
Cele przedmiotu
| KOD | Cel modułu/przedmiotu |
|---|---|
| C-1 | knowledge of basic algorithms for image data preprocessing from different modalities, i.e. visible band, thermography, near infrared (interpolation, quantisation, filtering) |
| C-2 | knowledge of selected methods for the extraction of low-level features from image data (i.e. brightness histogram, statistical features, textural features, colour features) and comparison with features extracted by deep learning methods |
| C-3 | knowledge of selected algorithms for classifying objects extracted from the scene as well as entire images (e.g. knn, mlp, dt, boosting) and comparison with deep learning methods |
Treści programowe z podziałem na formy zajęć
| KOD | Treść programowa | Godziny |
|---|---|---|
| wykłady | ||
| T-W-1 | Process of image data acquisition and pre-processing in computer systems | 2 |
| T-W-2 | Low-level feature extraction from image data | 2 |
| T-W-3 | Selected methods for learning and testing computer vision algorithms | 2 |
| T-W-4 | Overview of typical computer vision tasks: object detection, segmentation and tracking, stereovision, optical flow and background modelling | 2 |
| 8 | ||
Obciążenie pracą studenta - formy aktywności
| KOD | Forma aktywności | Godziny |
|---|---|---|
| wykłady | ||
| A-W-1 | participation in classes | 8 |
| A-W-2 | self-study of issues presented in class | 4 |
| A-W-3 | preparation for the credit | 2 |
| A-W-4 | participation in the consultations | 2 |
| 16 | ||
Metody nauczania / narzędzia dydaktyczne
| KOD | Metoda nauczania / narzędzie dydaktyczne |
|---|---|
| M-1 | informative lecture |
| M-2 | presentation |
| M-3 | problem-based lecture |
Sposoby oceny
| KOD | Sposób oceny |
|---|---|
| S-1 | Ocena podsumowująca: Final assessment in the form of a test |
Zamierzone efekty uczenia się - wiedza
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla dyscypliny | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| ISDE_4-_IEM07.2_W01 Students will have knowledge of the objectives, methods and applications of selected computer vision methods. As a result of the course, they should be able to define the elements of image processing pipeline from its acquisition, through processing to final analysis, and to select appropriate algorithms for certain types of data and tasks and explain and indicate, their characteristics. | ISDE_4-_W01, ISDE_4-_W02 | — | C-1, C-2, C-3 | T-W-1, T-W-2, T-W-3, T-W-4 | M-1, M-2, M-3 | S-1 |
Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla dyscypliny | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|
| ISDE_4-_IEM07.2_K01 as a result of the course, students will acquire the competence to critically analyse the results obtained in the field of computer vision and will develop an active cognitive attitude and a desire for scientific development | ISDE_4-_K01, ISDE_4-_K02 | — | C-1, C-2, C-3 | T-W-1, T-W-2, T-W-3, T-W-4 | M-1, M-2, M-3 | S-1 |
Kryterium oceny - wiedza
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| ISDE_4-_IEM07.2_W01 Students will have knowledge of the objectives, methods and applications of selected computer vision methods. As a result of the course, they should be able to define the elements of image processing pipeline from its acquisition, through processing to final analysis, and to select appropriate algorithms for certain types of data and tasks and explain and indicate, their characteristics. | 2,0 | |
| 3,0 | the student is able to assess the validity and applicability of appropriate image data preprocessing algorithms (interpolation, quantisation, filtering), selected methods for the extraction of low-level features (brightness histogram, statistical features, textural features) and selected classification algorithm (e.g. knn, mlp, dt or boosting) in typical computer vision tasks | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Kryterium oceny - inne kompetencje społeczne i personalne
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| ISDE_4-_IEM07.2_K01 as a result of the course, students will acquire the competence to critically analyse the results obtained in the field of computer vision and will develop an active cognitive attitude and a desire for scientific development | 2,0 | |
| 3,0 | the student is able to assess the validity and applicability of appropriate image data preprocessing algorithms (interpolation, quantisation, filtering), selected methods for the extraction of low-level features (brightness histogram, statistical features, textural features) and selected classification algorithm (e.g. knn, mlp, dt or boosting) in typical computer vision tasks | |
| 3,5 | ||
| 4,0 | ||
| 4,5 | ||
| 5,0 |
Literatura podstawowa
- C. Bishop, Pattern Recognition and Machine Learning, Springer Verlag, 2006
- R. Szeliski, Computer Vision: Algorithms and Applications, 2nd ed., The University of Washington, 2022, https://szeliski.org/Book/
- Simon J.D. Prince, Computer Vision: Models, Learning, and Inference, Cambridge University Press, 2012
Literatura dodatkowa
- Mark S. Nixon and Alberto S. Aguado, Feature Extraction & Image Processing for Computer Vision, Academic Press, 2019, 4, https://www.southampton.ac.uk/~msn/book/
- Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004, 2, https://www.robots.ox.ac.uk/~vgg/hzbook/
- Adrian Kaehler, Gary Bradski, Computer Vision in C++ with the OpenCV Library, O'Reilly, 2017, https://github.com/oreillymedia/Learning-OpenCV-3_examples
- Bharath Ramsundar, Reza Bosagh Zadeh, TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning, O'Reilly Media, 2018