Selected Topic: Machine Vision for Robotics and HCI
Course code: AT70.9011
This course is elective
Course objectivesMachine vision is concerned with the image processing, geometry, and statistical inference tools necessary for extracting useful information about the world from two-dimensional images. After decades of research, although the most advanced machine vision systems still pale in comparison to the visual systems of the simplest mammals, there have been some success stories. This course is an advanced survey of the state of the art in machine vision, focused primarily on robotics applications and human-computer interfaces. The course is a mixture of lectures on fundamentals, student presentations of research from the primary academic literature, and group projects involving application of machine vision technology to real-world problems. The course prepares students to do thesis research in the field.
Learning outcomeIntroduction. Projective geometry. Statistical estimation. Cameras. Two-view stereo. Three-view stereo. N-view reconstruction. Machine learning. Sequential state estimation. Applications. Programming in OpenCV and Octave/Matlab. Student presentations of primary research papers.
Prerequisite(s)Programming experience, mathematical sophistication.
II. Projective geometry
III. Statistical estimation
V. Two-view stereo
VI. Three-view stereoVII. N-view reconstruction
VIII. Machine learning
IX. Sequential state estimation
XI. Programming in OpenCV and Octave/Matlab
XII. Student presentations of research papers
TextbookHartley, Richard, and Zisserman, Andrew. Multiple View Geometry in Computer Vision, 2nd edition, Cambridge University Press, 2004.
JournalsInternational Journal of Computer Vision.
IEEE Transactions on Pattern Analysis and Machine Intelligence.
Image and Vision Computing.
Computer Vision and Image Understanding.
IEEE Transactions on Robotics.
Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).
Proceedings of the European Conference on Computer Vision (ECCV).
Proceedings of the Asian Conference on Computer Vision (ACCV).
Proceedings of the International Conference on Robotics and Automation (ICRA).
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Reference booksBishop, Christopher. Pattern Recognition and Machine Learning, Springer, 2006.
Trucco, Emanuele and Verri, Alessandro. Introductory Techniques for 3-D Computer Vision, 1st edition, Prentice Hall, 1998.
Forsyth, David A. and Ponce, Jean. Computer Vision: A Modern Approach, 1st edition, Prentice Hall, 2002.
Grading30% in-class presentations, 10% in-class discussion and presentation feedback, 10% online tutorial, 10% homework, 40% project.