In this class, students will learn about modern computer vision. The first part of the course will cover fundamental concepts such as image formation and filtering, edge detection, texture description, feature extraction and matching, grouping and clustering, model fitting, and combining multiple views. A crash course in machine learning will follow, in preparation for the second course chapter on visual recognition. We will study classic and modern approaches in object detection, deep learning, mid-level representations, active, transfer, and unsupervised learning, tracking, and human pose and activity recognition. The format will include lectures, homework assignments, exams, and a course project.

  • Credits: 3
  • Frequency: At least once a year


Requirements and Grading

• Homework (3 assignments) – 30%
• Project – 35% (proposal 5%, status report 5%, final presentation 5%, final report 20%)
• Midterm exam – 15%
• Final exam – 15%
• Participation – 5%