ECSE 6650 Computer
Vision
Instructor:
Dr. Qiang Ji,
Email: qji@ecse.rpi.edu
Phone:
276-6440
Office:
JEC 7004
Meeting Hours &
Place : 12:30-1:50 pm, Tues and Fridays, Vorhes South
Office Hours:
Tues and Fridays 2:00pm - 3:00pm pm or by Appointment
Lecture notes:
http://www.ecse.rpi.edu/~qji
This course deals with the science and
engineering of computer vision,
that is, the analysis of patterns in visual images of a 3D scene with the
goal of interpreting, understanding, and reconstructing the 3D scene.
The emphasis is on physical, mathematical, geometric and information processing
aspects of vision. Topics to be covered include image formation and
representation, feature extraction, camera calibration, image noise
representation and propagation, stereo vision, projective geometry, 3D
reconstruction, structure from motion, tracking, and analytical performance
characterization. In addition, the course will cover applications of
computer vision techniques for face detection and recognition, facial feature
tracking, eye tracking, facial expression understanding, and medical
image segmentatation. This course will
be very useful for students interested in human computer interaction, robotics,
photogrammetry, remote sensing, and medical imaging.
Required Text: Introductory Techniques for 3D Computer Vision Approach, Emanuele Trucco & Alessandro Verri.
The textbook is supplemented by frequent handouts and the materials from the following books.Recommended
Texts:
Three-Dimensional Computer Vision-a geometric viewpoint, Oliver
Faugeras, The MIT Press, 1993.
Multiple View Geometry in Computer Vision, Richard Hartley and Andrew
Zisserman, Cambridge, 2001.
Computer and Robot Vision, Robert M. Haralick and Linda G. Shapiro, Volumes 1 and 2, Addison-Wesley Publishing Company, 1993.
Method of
Evaluation:
Grading will be based on homework assignments, projects, a
middle-term
exam, and the final
project. There will be 4-5 projects, several assignments,
and a final project. The grade distribution is as follows:
Assignments: 15%
Projects: 50%
Midterm Exam: 20%
Final Project: 15%