Object Recognition

CS395T: Special Topics in Computer Vision, Spring 2010

Object Recognition

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Course overview        Useful links        Syllabus        Detailed schedule        eGradebook        Blackboard

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Meets: Wednesdays 3:30-6:30 pm
ACES 3.408
Unique # 54470
 
Instructor: Kristen Grauman 
Email: grauman@cs
Office: CSA 114
 
TA: Sudheendra Vijayanarasimhan
Email: svnaras@cs
Office: CSA 106

When emailing us, please put CS395 in the subject line.

Announcements:

See the schedule for current reading assignments.

Course overview:

Topics: This is a graduate seminar course in computer vision.   We will survey and discuss current vision papers relating to object recognition, auto-annotation of images, and scene understanding.  The goals of the course will be to understand current approaches to some important problems, to actively analyze their strengths and weaknesses, and to identify interesting open questions and possible directions for future research.

See the syllabus for an outline of the main topics we'll be covering.

Requirements: Students will be responsible for writing paper reviews each week, participating in discussions, completing one programming assignment, presenting once or twice in class (depending on enrollment, and possibly done in teams), and completing a project (done in pairs). 

Note that presentations are due one week before the slot your presentation is scheduled.  This means you will need to read the papers, prepare experiments, make plans with your partner, create slides, etc. more than one week before the date you are signed up for.  The idea is to meet and discuss ahead of time, so that we can iterate as needed the week leading up to your presentation. 

More details on the requirements and grading breakdown are here.

Prereqs:  Courses in computer vision and/or machine learning (378 Computer Vision and/or 391 Machine Learning, or similar); ability to understand and analyze conference papers in this area; programming required for experiment presentations and projects. 

Please talk to me if you are unsure if the course is a good match for your background.  I generally recommend scanning through a few papers on the syllabus to gauge what kind of background is expected.  I don't assume you are already familiar with every single algorithm/tool/image feature a given paper mentions, but you should feel comfortable following the key ideas.

Syllabus overview:

1. Single-object recognition fundamentals: representation, matching, and classification
1. Specific objects
   
2. Classification and global models
3. Objects composed of parts
4. Region-based methods
2. Beyond single objects: recognizing categories in context and learning their properties
1. Context
2. Attributes
3. Actions and objects/scenes
   
3. Scalability issues in category learning, detection, and search
1. Too many pixels!
2. Too many categories!
3. Too many images!
   
4. Recognition and "everyday" visual data
1. Landmarks, locations, and tourists
2. Alignment with text
3. Pictures of people

(생략)

Other useful links:

• Compiled list of recognition datasets
  
• OpenCV (open source computer vision library)
• Weka (Java data mining software)
• Netlab (Matlab toolbox for data analysis techniques, written by Ian Nabney and Christopher Bishop)
• CV Online
• Annotated Computer Vision Bibliography
• Computer vision conferences
• ICCV 2005 / CVPR 2007 / ICCV 2009 Short Course on Recognition
• AAAI 2008 Tutorial on Recognition
• ICML 2008 Tutorial on Recognition

 
 
Related courses:
 
Past semesters at UT:

• CS 395T Spring 2007: Object Recognition
• CS 395T Spring 2008: Visual Recognition and Search
• CS 395T Spring 2009: Visual Recognition and Search

 
By colleagues elsewhere:

• Object Recognition and Scene Understanding, MIT, Antonio Torralba
• Learning-based Methods in Vision, CMU, Alyosha Efros
• Selected Topics in Vision & Learning, UCSD, Serge Belongie
• Recognition Problems in Computer Vision, SFU, Greg Mori
• High-Level Recognition in Computer Vision, Princeton, Fei-Fei Li
• Computer Vision and the Web, UNC, Svetlana Lazebnik

reference
http://www.cs.utexas.edu/~grauman/courses/spring2010/schedule.html