Computer Vision

Data is displayed for academic year: 2023./2024.

Lecturers

Assoc. Prof.
Tomislav Hrkać
Assoc. Prof.
Zoran Kalafatić
Prof.
Siniša Šegvić

Lectures

Josip Šarić
PhD
Petra Bevandić
mag. ing.

Course Description

Geometric warps. Edge and corner detection. Scale-invariant key points. Motion analysis in the image plane. Convolutional models for classification. Object detection and semantic segmentation. Generative models. Differentiable modules based on attention.

Study Programmes

University graduate
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Learning Outcomes

  1. define concepts of computer vision and complex systems based on vision
  2. explain and distinguish porocedures, methods and algorithms related to image processing and computer vision.
  3. apply methods from the computer vision for robot vision applications
  4. analyze and breakdown/divide problem related to the computer vision or robot vision system
  5. design and develop the computer or robot vision system for the specific application
  6. evaluate quality of solution of the system based on computer vision

Forms of Teaching

Lectures

lectures

Seminars and workshops

discussion of selected research articles

Laboratory

Carrying out laboratory exercises

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 0 %
Mid Term Exam: Written 0 % 40 % 0 %
Final Exam: Written 0 % 40 %
Exam: Written 50 % 80 %

Week by Week Schedule

  1. Introduction.
  2. Geometric warps.
  3. Edge and corner detection.
  4. Scale-invariant key points.
  5. Motion analysis in the image plane.
  6. Discussion: Robust Real-Time Face Detection (IJCV 2004)
  7. Discussion: Semi-global matching (TPAMI 2007)
  8. Midterm exam
  9. Convolutional models for classification.
  10. Object detection and semantic segmentation.
  11. Generative models.
  12. Differentiable modules based on attention.
  13. Discussion: Understanding deep learning requires rethinking generalization (ICLR 2017).
  14. Discussion: Masked Autoencoders Are Scalable Vision Learners (CVPR 2022).
  15. Final exam

Literature

R. Szeliski (2011.), Computer Vision: Algorithms and Applications, Springer
D. A. Forsyth, J. P. Ponce (2003.), D. A. Forsyth, J. P. Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2003., Prentice Hall

For students

General

ID 222674
  Winter semester
5 ECTS
L0 English Level
L1 e-Learning
45 Lectures
0 Seminar
0 Exercises
0 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

89 Excellent
76 Very Good
63 Good
50 Sufficient

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