(report 30%, results 40%, and code 30%) syllabus


                           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, Mondays and Thursdays, JEC 4304
Office Hours: Tuesdays 2:00pm - 3:00pm pm   or by Appointment
TA : Ruixuan Yan, yanr5@rpi.edu
TA Office Hours: Tuesdays 5:00pm - 6:00pm pm, ECSE Lounge
Lecture notes:   http://www.ecse.rpi.edu/~qji/CV/ecse6650_lecture_notes.html

This course covers core computer vision theories that deal with acquiring, processing and analyzing images in order to reconstruct and understand the 3D scene. It will focus on the mathematical models that map a 3D scene to its 2D images, theories that reconstruct and interpret the 3D scene from their images, and methods for image feature extraction. Topics to be covered include image formation and representation, camera models, projective geometry, camera calibration, pose estimation, 3D reconstruction, motion analysis, structure from motion, target tracking, feature extraction, and object recognition. Besides computer vision, this course will also be useful for students interested in pattern recognition, image processing, robotics, human computer interaction, and medical imaging.

Prerequisites: A good background in linear algebra, statistics, programming in Python, C++ or MATLAB

TextBook: No formal textbook but detailed lecture notes will be provided

Optional Texts:

Computer Vision: Algorithms and Applications Richard Szeliski

Introductory Techniques for 3D Computer Vision Approach, Emanuele Trucco & Alessandro Verri.

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.

Probabilistic graphical models for computer vision, Qiang Ji, Qiang Ji , Academic Press, 2019.

Method of Evaluation:
   Grading will be based on homework assignments, projects, a middle-term
exam, and the final project. There will be 4-5 class projects , several assignments,
and a final project. The grade distribution is as follows:

Assignments: 20%
Projects: 40% (report 30%, results 40%, and code 30%)
Midterm Exam: 25%
Final Project: 15%

Lecture Notes