Units:

12

Description:

This course provides students with a solid foundation in the key elements of computer vision, emphasizing the practical application of the underlying theory. It focuses mainly on the techniques required to build robot vision applications but the algorithms can also be applied in other domains such as industrial inspection and video surveillance. A key focus of the course is on effective implementation of solutions to practical computer vision problems in a variety of environments using both bespoke software authored by the students and standard computer vision libraries.

The course covers optics, sensors, image formation, image acquisition & image representation before proceeding to the essentials of image processing and image filtering. This provides the basis for a treatment of image segmentation, including edge detection, region growing, and boundary detection, color-based segmentation, as well as more sophisticated techniques such as snakes and graph-cuts.

Building on this, the course then proceeds to deal with object detection and recognition in 2D, addressing interest point operators, gradient orientation histograms, the SIFT descriptor, color histogram intersection and back-projection, the Hough transform, template matching, and Bayesian classification.

Video image processing focuses on the detection and tracking of moving object using a variety of techniques, ranging from several types of background subtraction, optical flow, and the Kalman filter.

The problem of recovery of 3D information is then addressed, introducing homogeneous coordinates and transformations, the perspective transformation, camera model, inverse perspective transformation, stereo vision, and epipolar geometry, as well as other depth cues.

Visual attention, dealing with the question of what to look for and where to look for it in a scene, is then covered from both a human and computational perspective.

The course finishes by addressing the important role played by machine learning in computer vision today.

Last Modified: 2024-11-22 1:51PM

Semesters offered:

• Spring 2025
• Spring 2024
• Fall 2022
• Fall 2021
• Fall 2019
• Fall 2018