Announcements
- (Nov 17) We will be having an optional 2nd round of peer-reviews for your manuscripts. Please hand in an update copy on Nov 24 if you are interested.
- (Nov 14) Please add "\pagestyle{empty}" to the top of you latex file in order to remove the page numbers from your manuscript.
- (Nov 10) Please review your assigned manuscripts using the standard review form. Send me an electronic copty for each of your manuscripts before 9am on Tuesday, Nov 17.
Introduction
This course focuses on computer perception: using computers to analyze images, sounds, and videos. We will specifically focus on object recognition and multimedia retrieval, but will also look at segmentation, localization, clustering, tracking and other perception tasks.
The first third of this class will be a lecture style format that introduces some fundamental topics and tools. The remaining two thirds will be a seminar style format in which students will present academic papers and conduct research.
Class information
Professor: Douglas Turnbull
Office: Science Center 255
Phone: (610) 597-6071
Office hours: TBA or by appointment
Room: Science Center Conference Room
Time: Tuesday, Thursday 9:55pm–11:10pm
Text: None, but lots of suggested references and weekly readings...
Schedule
Grading
This course is structured like a graduate seminar course where each student will be graded based on both their contribution to the seminar and their
research project.
| Course Work | 40% |
| Lab 1 | 10% |
| Assigned Paper Presentation | 10% |
| Weekly Notes | 20% |
|
| Project | 60% |
| Proposal | 5% |
| Proposal Update | 5% |
| Literature Review Presentation | 10% |
| Manuscript | 10% |
| Manuscript Reviews | 5% |
| Conference Presentation | 10% |
| Final Paper | 15% |
|
You will automatically get an A if you get your research paper accepted to a top-tier, peer-reviewed academic conference.
Weekly Notes
For every academic paper that we read for class, you should prepare a 1-page summary. The format should be as follows:
- Summary: 3-4 sentences that describe what the paper is about. What is the problem? How do the authors attempt solve the problem? What do they conclude from the results?
- Strengths: a list of 3 strengths of the paper. Is the solution novel and interesting? Are the results compelling? Is the evaluation clear and illustrative? Is the paper successful?
- Weaknesses: a list of three weaknesses. Are there any hole in the logic of the paper? Are the result dubious? Do the authors make any questionable assumption?
- Questions: a list of three (or more) things that you found confusing. We will be covering some pretty dense papers this semester. It usually takes a couple of readings before a paper become comprehensible. Evem still, do not be intimidated if you only understand a small portion of each paper. With time and experience, you will get better at understanding the material.
Academic Integrity
Academic honesty is required in all work you submit to be
graded. With the exception of your lab partner on lab
assignments, you may not submit work done with (or by)
someone else, or examine or use work done by others to
complete your own work.
You may discuss assignment specifications and requirements
with others in the class to be sure you understand the
problem. In addition, you are allowed to work with others to
help learn the course material. However, with the exception
of your lab partner, you may not work with others on your
assignments in any capacity.
All code you submit must be your own with the following
permissible exceptions: code distributed in class, code
found in the course text book, and code worked on with an
assigned partner. In these cases, you should always include
detailed comments that indicates which parts of the
assignment you received help on, and what your sources were.
``It is the opinion of the faculty that for an intentional
first offense, failure in the course is normally
appropriate. Suspension for a semester or deprivation of
the degree in that year may also be appropriate when
warranted by the seriousness of the offense.'' - Swarthmore
College Bulletin (2007-2008, Section 7.1.2)
Please see me if there are any questions about what is permissible.
External Links
Links that are related to the course may be posted here. If you have suggestions for links, let me know.
Machine Learning and Pattern Recognition
Image and Audio Processing
Matlab - General Info
Matlab - Computer Perception
Other Software (Weka, Matlab, etc.)
- Weka - Java-based Machine Learning Software
- Marysas - Music Signal Processing in Java
- Echonest Music API - content and context based music information