Computer Vision
Explore theoretical and practical aspects of computer vision, covering classical and modern deep-learning approaches. Topics range from the basics of the image formation process in digital cameras and biological systems to mathematical and practical treatments of basic image processing. Examine space and frequency representations, classical computer vision techniques for making three-dimensional measurements from images, and state-of-the-art deep-learning models for image classification and recognition.
Topics
- Analyze and evaluate the process by which images are formed and represented through implementing prospective projection, camera calibration, and Bayer demosaicing Analyze and implement the building blocks of classical computer vision techniques, including filtering, edge detection, Fourier analysis, histogram equalization, feature extraction, and matching. Analyze and implement the building blocks of modern computer vision techniques, including convolution, dimensionality reduction, neural networks, and optimization. Properly formulate computer vision problems and solutions, selecting the appropriate mathematical and computational tools to analyze images and videos. Design and build computer-vision systems and pipelines that solve applied problems in the computer vision space, including image classification. Evaluate contemporary computer vision approaches by assessing the validity of proposed methodologies, experimental setups, and technical contributions.
Official catalogue course objectives and student learning outcomes
Prerequisites & course information
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Sources
- Course catalogue ↗2026-2027