This course is an introduction to machine vision. In this course, selected topics in machine vision will be taught including the modeling of optical sensors, projection geometry, image features such as color and texture, as well as techniques for solving specific problems such as object recognition and tracking and 3D reconstruction of space from images. In more detail, the course includes topics such as: elements of photometry, color and image creation. Optical sensor modeling, lenses, camera calibration. Finding and clustering local image features. Image segmentation and pattern recognition. Emphasis is placed on stereoscopic vision, epipolar geometry and optical flow, space reconstruction from multiple images. Movement, visual flow and object tracking.

The expected learning outcomes for students who will attend the course are as follows:

• Understanding of the most important subject areas of machine vision.
• Gain knowledge about the state-of-the-art in the most important areas of machine vision.
• Develop critical thinking and the ability to propose solutions and conduct research on topics related to machine vision.
• Development of skills in the use of specific tools for the solution of machine vision problems.

Required knowledge: Computer Science, Computer Science for Engineers, Advanced Programming.