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E C E 766

COMPUTER VISION

3 credits.

Fundamentals of image analysis and computer vision; image acquisition and geometry; image enhancement; recovery of physical scene characteristics; shape-from techniques; segmentation and perceptual organization; representation and description of two-dimensional objects; shape analysis; texture analysis; goal-directed and model-based systems; parallel algorithms and special-purpose architectures. Students are strongly encouraged to have basic proficiency in calculus and linear algebra, such as MATH 340 , and basic programming such as COMP SCI 300 .

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Develop basic computer vision applications using a programming environment Audience: Graduate 2. Formulate computer vision research problems motivated from real-world applications Audience: Graduate 3. Evaluate and compare existing solutions to a computer vision problem Audience: Graduate 4. Design approaches for solving computer vision problems based on a broad range of fundamental concepts in 2D and 3D computer vision, sensing and recognition Audience: Graduate 5. Communicate solutions verbally and in writing to justify choices while designing solutions Audience: Graduate Learning Outcomes: 1. Develop basic computer vision applications using a programming environment Audience: Graduate 2. Formulate computer vision research problems motivated from real-world applications Audience: Graduate 3. Evaluate and compare existing solutions to a computer vision problem Audience: Graduate 4. Design approaches for solving computer vision problems based on a broad range of fundamental concepts in 2D and 3D computer vision, sensing and recognition Audience: Graduate 5. Communicate solutions verbally and in writing to justify choices while designing solutions Audience: Graduate

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