Computer Vision Applications

Course	T-869-COMP
Semester	20243
ETCS	6
Core	No

Year	1. year
Semester	Fall 2024
Level of course	5. Second cycle, intermediate
Type of course	Elective
Prerequisites	No prerequisites.
Schedule	No schedule found.
Lecturer	Torfi Þórhallsson
Content
Image formation, cameras and projection models, low-level image processing methods such as filtering, edge detection, interest operators, optical flow; mid-level vision topics such as model fitting and image-to-image matching; shape and motion estimation from multiple cameras, multiple-view constraints, probabilistic models and MAP estimationt, robust estimation using RANSAC; high-level vision tasks such as object and scene recognition, tracking using dynamic models and Kalman filtering.
Learning outcome - Objectives
After the course the students should be able to recall, describe and define, the following terms:  Image formation, cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization.After the course the students should be able to use Open CV and/or other real-time computer vision tools to acquire image data and implementcomputer vision algorithms to detect and recognize facial expressions and apply these techniques to emotion classification.   After the course the students should be able to design a suitable computer vision algorithm and recognition techniques for real world problems,evaluate algorithmic performance and compare different designs and implementations and interpret the results.  The students should also be able to present findings and new results in the subject.
Course assessment
Reading material
No reading material found.
Teaching and learning activities
Language of instruction	English