CAP4453-Robot Vision (SPRING 2018)
Class time: Monday/Wednesday 10.30-11.45 am
Class location: HEC 0111
Office hours: Monday/Wednesday 12-1 pm
Please put [CAP4453] in the subject line of your emails.
The study of mechanical vision is one of the few areas of science which blends one’s intuition with formal methods. Vision (whether in humans or machines) is fundamentally a computational process. Visual processes for machines must be able to deliver the kinds of capabilities that humans have: scene recognition, motion processing, navigational abilities, and so forth. This course will begin by examining some of the elementary concepts in machine vision. Subprocesses to be examined include: edge detection, methods for obtaining shape information from images, object detection, and motion analysis. The student will also be exposed to unsolved problems in these topics, the solutions to which have very high technological pay-offs.
The workload consists of interesting reading, programming, and tests. First two programming assignments will be introductory level, while the last two will be project based, and it will be intense exposure to one sub-area of machine vision.
This class is suitable for undergradaute students in Computer Science and Engineering disciplines, and anyone else who wishes an introduction to machine vision.
Basic Probability/Statistics, a good working knowledge of any programming language (python, matlab, C/C++, or Java), Linear algebra and/or Vector calculus.
GRADING:Assignments and the term project should include explanatory/clear comments as well as a short report describing the approach, detailed analysis, and discussion/conclusion.
RECOMMENDED BOOKS (optional)
Simon Prince, Computer Vision: Models, Learning, and Interface, Cambridge University Press,
Mubarak Shah, Fundamentals of Computer Vision,
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010 (online draft),
Forsyth and Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2002,
Palmer, Vision Science, MIT Press, 1999,
Duda, Hart and Stork, Pattern Classification (2nd Edition), Wiley, 2000,
Koller and Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009,
Strang, Gilbert. Linear Algebra and Its Applications 2/e, Academic Press, 1980.
- 4 Programming assignments 40% (10% each)
- Midterm Exam 20% (in-class, written)
- Final Exam 40% (in-class, written)
- Letter Grades: 95-100 (A), 90-94 (A-), 85-89 (B+), 80-84 (B), 75-79 (B-), 70-74 (C+), 65-69 (C), 60-64 (C-), 50-59 (D), 0-49 (F)
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Python will be main programming environment for the assignments. Following book (Python programming samples for computer viion tasks) is freely available.
Python for Computer Vision
Collaboration on assignments is encouraged at the level of sharing ideas and technical conversation only. Please write your own code. Students are expected to abide by UCF Golden Rule.
| Mailing address:
Dr. Ulas Bagci
Center for Research in Computer Vision (CRCV)
4328 Scorpius Street, HEC 221, UCF
Orlando, Florida 32816, USA.
Last updated December, 2017 by Ulas Bagci.