This course provides an overview of Computer Vision – enabling computers to understand visual input. The course introduces the major concepts and methods, and gives you a sample of problem settings and techniques for solving them. It is intended for those who want to get an understanding of how computer vision works and how it can perform such diverse tasks as object recognition, robot navigation, and increase situational awareness. The course has strong ties between theory, examples of its application, and practical exercises in order to promote fast and persistent learning.
The course is intended for students with a basic proficiency in linear algebra and a programming
language.
Example courses at NPS: MA2043 or MA3042
What tasks do you need to master in order to achieve the overall course goal? Upon successful completion of this course, you will be able to:
Take a look at the Contents on the following pages to get an idea of the syllabus. Computer vision is a “hot topic” and rapidly evolving, so expect several modifications that reflect recent developments and advances. Every chapter is a learning module with specific learning objectives, reading and study material, exercises, and graded assignments.
There is no required textbook for this class. We will work from a reader and several resources provided
to you in class. Grades for homework assignments etc. will be on Blackboard. Software and other
course material can be found here:
http://www.movesinstitute.org/~kolsch/courses/CS4330
and here:
\\comfort.ern.nps.edu\cs4330$
Lectures and lab:
(TBA) Monday 1500-1650, WA-275
(TBA) Wednesday 1500-1750, WA-275
These two time slots will be used for lectures and/or lab time. Please bring your laptop for every
meeting.
Final:
Monday 12/10/07, 0800-0950 Bu-202
Holidays:
Mon 8 Oct 2007 (Columbus Day),
Mon 12 Nov 2007 (Veteran’s Day),
Thu 22 Nov 2007 (Thanksgiving)
Office Hours:
I am available for questions and help whenever I am in my office (WA-279) and have some time. If you would like a firm appointment, please don’t hesitate to set one up by email or phone (656-3402).
Grades for homework assignments etc. will be on Blackboard:
50% lab projects incl. reports
10% homework assignments, surprise quizzes (if any)
15% midterm
25% final
Homework assignments will be posted on or before Mondays and are due the following Thursday at 11:59pm unless otherwise noted. No credit will be given for assignments that are more than 3 days late (Sunday 11:59pm). You can hand in at most one assignment up to 3 days late and still receive full credit. That is: one permitted late assignment for the entire quarter. Additional late assignment will be given a max of 50% credit.
Unless otherwise noted, written assignment can be handed in on a sheet of paper (preferred) or by email. If you email it to me, please make sure your name appears on top of the actual assignment (e.g., in the text file attachment), not just in the email. Programming assignment must be turned in using Blackboard’s Digital Dropbox. See the respective assignment assignment for the naming convention.
Collaborative work:
There will be weekly reading assignments from the reader and from other reading material. A “reading assignment” requires you to read the material and to write a short, one or two page summary. Demonstrate your understanding of the contents, don’t provide me with a complete recount of the entire text. You are encouraged to add a paragraph in which you critique the reading, note questions that you have or state some other related comment. Print the reports (duplex, please) and bring them to class on the respective due day. If you cannot make it to class, please email them to me prior to class.
All special equipment and software will be provided to you:
High-performance laptop, Matlab with Image Processing Toolbox, USB or FireWire (1394) camera,
OpenCV
As part of the course, we will work on small programming projects with the help of a high-level computer vision library (OpenCV) to demonstrate some of the methods we have learned in class. Some sample projects are listed below, further ideas and detail can be found in Appendix C:
Textbooks, more or less in order of relevance (res: on reserve in the library)