Vision is one of the major senses that enables humans to act (and interact) in (ever)changing environments, and imaging is the means by which we record visual information in a form suitable for computational processing. Together, imaging and computer vision play an important role in a wide range of intelligent systems, from advanced microscopy techniques to autonomous vehicles. This module will focus on the fundamental computational principles that enable an array of picture elements, acquired by one of a multitude of imaging technologies, to be converted into structural and semantic entities necessary to understand the content of images and to accomplish various perceptual tasks. We will study the problems of image formation, low level image processing, object recognition, categorisation, segmentation, registration, stereo vision, motion analysis, tracking and active vision. The lectures will be accompanied by a series of exercises in which these computational models will be designed, implemented and tested in real-world scenarios.
Learning Outcomes
By the end of the module students should be able to:
Understand the main computer vision and imaging methods and computational models
Design, implement and test computer vision and imaging algorithms
Know how to synthesise combinations of imaging and vision techniques to solve real-world problems
The student should demonstrate the capacity to independently study, understand, and critically evaluate advanced materials or research articles in the subject areas covered by this module.
