INTRODUCTION TO COMPUTER VISION
Topics include image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, and automatic vehicle navigation. Broad overview of various computer vision and machine learning techniques and sensing and imaging technologies used in computer vision applications. Project-based.
Topics
- image formation
- feature detection
- motion estimation
- image mosaics, 3D shape reconstruction
- and object recognition
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: COMP SCI 400 and ( MATH 320 , 340 , 341 , 345 or 375 ) and ( STAT 311 , 324 , 333 , 340 , 371 , STAT/MATH 309 , 431 , MATH 331 or 531 ) or graduate/professional standing Course Designation: Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Describe a broad range of fundamental concepts in 2D and 3D computer vision, including basic image processing, 2D image recognition, 3D sensing and motion recovery Audience: Undergraduate 2. Explain basic theories, current approaches, key concepts, and common practices in computer vision. Audience: Undergraduate 3. Design basic statistics and machine learning algorithms for computer vision problems, conduct experiments to evaluate the proposed approaches, and analyze and interpret the results. Audience: Undergraduate 4. Develop basic computer vision applications and projects in MATLAB. Audience: Undergraduate 5. Prepare and deliver clear and concise oral presentations. Audience: Undergraduate Learning Outcomes: 1. Describe a broad range of fundamental concepts in 2D and 3D computer vision, including basic image processing, 2D image recognition, 3D sensing and motion recovery Audience: Undergraduate 2. Explain basic theories, current approaches, key concepts, and common practices in computer vision. Audience: Undergraduate 3. Design basic statistics and machine learning algorithms for computer vision problems, conduct experiments to evaluate the proposed approaches, and analyze and interpret the results. Audience: Undergraduate 4. Develop basic computer vision applications and projects in MATLAB. Audience: Undergraduate 5. Prepare and deliver clear and concise oral presentations. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027