Computer Vision
Data Science
281
3 units
Course Description
This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3-D measurements from images, and modern deep-learning based techniques for image classification and recognition.
Student Learning Outcomes
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Be able to read and understand research papers in the computer-vision literature.
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Build computer vision systems to solve real-world problems.
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Properly formulate problems with the appropriate mathematical and computational tools.
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Understand the building blocks of classical computer vision techniques.
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Understand the building blocks of modern computer vision techniques (primarily artificial neural networks).
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Understand the process by which images are formed and represented.
