School of Engineering and Technology, (SET) | ||
AT70.20 : Machine Vision for Robotics and HCI 3(3-0) | ||
Course objectives: | ||
Machine 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. |
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Learning Outcomes: | ||
Introduction. 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. |
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Pre-requisite(s): | ||
Programming experience, mathematical sophistication. |
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Course Outline: | ||
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Laboratory Sessions: | ||
None |
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Learning Resources: | ||
Textbook: | ||
Hartley, Richard, and Zisserman, Andrew. Multiple View Geometry in Computer Vision, 2nd edition, Cambridge University Press, 2004.
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Reference Books: | ||
Bishop, 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.
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Journals and Magazines: | ||
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Evaluation Scheme: | ||
In-class presentations - 30%
In-class discussion and presentation feedback - 10%
Online tutorial - 10%
Homework - 10%
Project - 40%
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Instructor(s): | ||
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