Popis predmeta

Course Description

Basic definitions of computer vision and robot vision.Tasks for a computer vision. Relation between biological, robot vision systems and relation to other fields. Image formation:Sampling lattices, Reciprocal lattices, 2D Shannon theorem Image geometry, Perspective projection, Model of camera. Calibration procedures. Stero imaging. Image input and representation in a computer. Binary image processing. Thresolding. Uspoređivanje s pragom. Histogram. Discrete binary image. Template maching.Topological properties of image. Size, position and orientation of object. Component labeling. BF-algorithm. Fourier coefficients. Granlund descriptors. Moments. Illustration of procedures and problem solving. Midterm exam Image segmentation. Region segmentation. Split and Merge method. Otsu method for threshold selection. Heuristic methods. Region representation. Edge segmentation. Gaussian. Sobel and compass operators. Canny operator. LoG operator. Illustration of procedures and problem solving. Border detection.Hough transform. Generalized Hough transform. Image Understanding Models. Hierarchical and Hetrrarchical Models. Blackboard model. Description formalisms. Knowledge representation in robot and computer vision systems. Illustration of procedures and problem solving. Final exam.

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

Seminars and workshops

Week by Week Schedule

  1. Image formation, Image processing, Recognition in multimedia documents (text, speech, audio-video)
  2. Image formation, Sampling, Antialiasing, Projection fundamentals and image formation
  3. Image formation, Calibration and rectification
  4. Image processing, Image classification
  5. Image processing
  6. Segmentation, Object recognition
  7. Segmentation, Stereo
  8. Midterm exam
  9. Feature detection and matching, Segmentation
  10. Feature detection and matching, Feature-based alignment
  11. Structure from motion
  12. Motion estimation
  13. Motion estimation, Biometric-based person identification
  14. Segmentation, Object detection and localization, Object tracking
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (1. semester) (3. semester)
Communication and Space Technologies (profile)
Free Elective Courses (1. semester) (3. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Science (profile)
Elective Courses of the Profile (1. semester) (3. semester)
Control Systems and Robotics (profile)
Free Elective Courses (1. semester) (3. semester)
Data Science (profile)
Free Elective Courses (1. semester) (3. semester)
Electrical Power Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (1. semester) (3. semester)
Electronic and Computer Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Electronics (profile)
Free Elective Courses (1. semester) (3. semester)
Information and Communication Engineering (profile)
Elective Courses of the Profile (1. semester) Elective Coursesof the Profile (3. semester)
Network Science (profile)
Free Elective Courses (1. semester) (3. semester)
Software Engineering and Information Systems (profile)
Elective Course of the profile (3. semester) Elective Course of the Profile (1. semester)

Literature

(.), M. Nixon, A. Aguado;Feature Extraction&Image Processing, Elsevier, 2008, 978-0750650786 ,
(.), D. A. Forsyth, J. P. Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2003. 978-0130851987,
(.), R. Jain, R. Kasturi, B. G. Schunck, Machine Vision, Pearson-Prentice Hall, 2003.978-0070320185 ,
(.), R. Szelinski, Computer Vision, Springer, 2011, 978-1-84882-934-4,

For students

General

ID 222674
  Winter semester
5 ECTS
L3 English Level
L1 e-Learning
45 Lectures

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

Seminars and workshops

Week by Week Schedule

  1. Image formation, Image processing, Recognition in multimedia documents (text, speech, audio-video)
  2. Image formation, Sampling, Antialiasing, Projection fundamentals and image formation
  3. Image formation, Calibration and rectification
  4. Image processing, Image classification
  5. Image processing
  6. Segmentation, Object recognition
  7. Segmentation, Stereo
  8. Midterm exam
  9. Feature detection and matching, Segmentation
  10. Feature detection and matching, Feature-based alignment
  11. Structure from motion
  12. Motion estimation
  13. Motion estimation, Biometric-based person identification
  14. Segmentation, Object detection and localization, Object tracking
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (1. semester) (3. semester)
Communication and Space Technologies (profile)
Free Elective Courses (1. semester) (3. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Science (profile)
Elective Courses of the Profile (1. semester) (3. semester)
Control Systems and Robotics (profile)
Free Elective Courses (1. semester) (3. semester)
Data Science (profile)
Free Elective Courses (1. semester) (3. semester)
Electrical Power Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (1. semester) (3. semester)
Electronic and Computer Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Electronics (profile)
Free Elective Courses (1. semester) (3. semester)
Information and Communication Engineering (profile)
Elective Courses of the Profile (1. semester) Elective Coursesof the Profile (3. semester)
Network Science (profile)
Free Elective Courses (1. semester) (3. semester)
Software Engineering and Information Systems (profile)
Elective Course of the profile (3. semester) Elective Course of the Profile (1. semester)

Literature

(.), M. Nixon, A. Aguado;Feature Extraction&Image Processing, Elsevier, 2008, 978-0750650786 ,
(.), D. A. Forsyth, J. P. Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2003. 978-0130851987,
(.), R. Jain, R. Kasturi, B. G. Schunck, Machine Vision, Pearson-Prentice Hall, 2003.978-0070320185 ,
(.), R. Szelinski, Computer Vision, Springer, 2011, 978-1-84882-934-4,

For students

General

ID 222674
  Winter semester
5 ECTS
L3 English Level
L1 e-Learning
45 Lectures