This course is an elective for both the Smart Systems and Visual Computing and Games Streams.

Lab	In addition to classes, this course has six 3-hour structured laboratory sessions per semester.

Prerequisites:  COMP 3301 or Engineering 7854 or permission of the instructor

Availability: This course is usually offered once per year, in Fall or Winter.

Course Objectives

This course studies how to develop methods that enable a machine to “understand” or analyze images. The course introduces the fundamental problems in computer vision and the state-of-the- art approaches that address them. Topics include feature detection and matching, geometric and multi-view vision, structure from X, segmentation, object tracking and visual recognition.

Representative Workload

• Assignments (2) 20%
• In-class Exam 20%
• Project 30%
• Final Exam 30%

Representative Course Outline

• Unit 1: Grouping and fitting (4 hours)
• K-means
• Hough transform
• RANSAC
• Unit 2: Feature detection and matching (4 hours)
• Interest point detection (corners/blobs)
• SIFT
• HOG
• Unit 3: Geometric and multi-view vision (4 hours)
• Geometric transformation
• Camera model and camera calibration
• Image stitching
• Unit 4: Feature based alignment (4 hours)
• 2D and 3D feature based alignment
• Pose estimation
• Unit 5: Structure from X (4 hours)
• Epipolar geometry
• Stereo vision
• Essential and fundamental matrix
• Structure from motion
• Unit 6: Segmentation and tracking (4 hours)
• Foreground segmentation in video
• Optical flow
• Tracking
• Unit 7: Recognition (4 hours)
• Introduction to recognition
• Object detect and recognition (face detection, pedestrian recognition)
• General category recognition (bags of features)

Notes

• Credit cannot be obtained for both Computer Science 4301 and Engineering 8814.

Page last updated May 24th 2021