Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Szkoła Doktorska - ZUT Doctoral School
specjalność: ENGINEERING AND NATURAL SCIENCES 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

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW6 8 0,51,00zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Fundamentals of linear algebra
W-2Fundamentals of probability calculus
W-3Fundamentals of algorithmics and numerical methods
W-4practical knowledge of the selected programming language: C/C++, Python, Matlab

Cele przedmiotu

KODCel modułu/przedmiotu
C-1knowledge of basic algorithms for image data preprocessing from different modalities, i.e. visible band, thermography, near infrared (interpolation, quantisation, filtering)
C-2knowledge 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-3knowledge 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

KODTreść programowaGodziny
wykłady
T-W-1Process of image data acquisition and pre-processing in computer systems2
T-W-2Low-level feature extraction from image data2
T-W-3Selected methods for learning and testing computer vision algorithms2
T-W-4Overview of typical computer vision tasks: object detection, segmentation and tracking, stereovision, optical flow and background modelling2
8

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
wykłady
A-W-1participation in classes8
A-W-2self-study of issues presented in class4
A-W-3preparation for the credit2
A-W-4participation in the consultations2
16

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1informative lecture
M-2presentation
M-3problem-based lecture

Sposoby oceny

KODSposób oceny
S-1Ocena podsumowująca: Final assessment in the form of a test

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób 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-_W02C-1, C-2, C-3T-W-1, T-W-2, T-W-3, T-W-4M-1, M-2, M-3S-1

Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób 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-_K02C-1, C-2, C-3T-W-1, T-W-2, T-W-3, T-W-4M-1, M-2, M-3S-1

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium 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
3,0the 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 sięOcenaKryterium 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
3,0the 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

  1. C. Bishop, Pattern Recognition and Machine Learning, Springer Verlag, 2006
  2. R. Szeliski, Computer Vision: Algorithms and Applications, 2nd ed., The University of Washington, 2022, https://szeliski.org/Book/
  3. Simon J.D. Prince, Computer Vision: Models, Learning, and Inference, Cambridge University Press, 2012

Literatura dodatkowa

  1. 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/
  2. Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004, 2, https://www.robots.ox.ac.uk/~vgg/hzbook/
  3. Adrian Kaehler, Gary Bradski, Computer Vision in C++ with the OpenCV Library, O'Reilly, 2017, https://github.com/oreillymedia/Learning-OpenCV-3_examples
  4. Bharath Ramsundar, Reza Bosagh Zadeh, TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning, O'Reilly Media, 2018

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Process of image data acquisition and pre-processing in computer systems2
T-W-2Low-level feature extraction from image data2
T-W-3Selected methods for learning and testing computer vision algorithms2
T-W-4Overview of typical computer vision tasks: object detection, segmentation and tracking, stereovision, optical flow and background modelling2
8

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1participation in classes8
A-W-2self-study of issues presented in class4
A-W-3preparation for the credit2
A-W-4participation in the consultations2
16
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_IEM07.2_W01Students 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.
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_W01The PhD students have extended, theory-based knowledge, enabling the discussion and analysis of existing paradigms with regard to the latest scientific developments, in particular related to the represented field and discipline.
ISDE_4-_W02They have extended, theory-based knowledge relating to the represented field and discipline and detailed knowledge at an advanced level in the area of scientific research ,methodology of scientific work, preparation of publications and presentations of research results and the principle of dissemination of the results of scientific work, including open access mode.
Cel przedmiotuC-1knowledge of basic algorithms for image data preprocessing from different modalities, i.e. visible band, thermography, near infrared (interpolation, quantisation, filtering)
C-2knowledge 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-3knowledge 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 programoweT-W-1Process of image data acquisition and pre-processing in computer systems
T-W-2Low-level feature extraction from image data
T-W-3Selected methods for learning and testing computer vision algorithms
T-W-4Overview of typical computer vision tasks: object detection, segmentation and tracking, stereovision, optical flow and background modelling
Metody nauczaniaM-1informative lecture
M-2presentation
M-3problem-based lecture
Sposób ocenyS-1Ocena podsumowująca: Final assessment in the form of a test
Kryteria ocenyOcenaKryterium oceny
2,0
3,0the 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
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_IEM07.2_K01as 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
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_K01They understand the necessity and are prepared to critically analyse the achieved scientific output and the contribution of the results of their own research activity to the development of the represented field and discipline.
ISDE_4-_K02They understand the obligation to seek creative solutions to the challenges of civilisation, in particular to social, research and creative commitments, are aware of the need to initiate actions in the public interest, to think in the entrepreneurial manner and the need for scientific development for new phenomena and problems in the represented field and discipline.
Cel przedmiotuC-1knowledge of basic algorithms for image data preprocessing from different modalities, i.e. visible band, thermography, near infrared (interpolation, quantisation, filtering)
C-2knowledge 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-3knowledge 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 programoweT-W-1Process of image data acquisition and pre-processing in computer systems
T-W-2Low-level feature extraction from image data
T-W-3Selected methods for learning and testing computer vision algorithms
T-W-4Overview of typical computer vision tasks: object detection, segmentation and tracking, stereovision, optical flow and background modelling
Metody nauczaniaM-1informative lecture
M-2presentation
M-3problem-based lecture
Sposób ocenyS-1Ocena podsumowująca: Final assessment in the form of a test
Kryteria ocenyOcenaKryterium oceny
2,0
3,0the 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