Computer Vision

| January 9, 2019 | 0 Comments

This course gives a broad overview of the field of computer vision, laying the foundations for advanced graduate level classes and research work in vision. This course will be conducted with an application perspective. Therefore students will be expected to implement several techniques learnt in the lectures. A good calculus, linear algebra and programming background is expected for this class. Knowledge of probability and random variables is also needed to understand the ideas presented in some modules.

The nature of the field of Computer Vision is such that it combines and integrates ideas from several different areas, including statistics, pattern recognition, machine intelligence, decision theory and image processing. Therefore, in an introductory class, it is not possible to cover each of these aspects in depth. Instead, the focus of this course is on breadth, presenting several different techniques and systems in moderate detail, so as to familiarize the student with the Computer Vision area in general, and to present some specific examples of Computer Vision systems.

Course Outline: PDF

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