Stanford University

CS 223B: Introduction to Computer Vision

Winter 2010-2011

Announcements:

• Details for Project submission::
Please email the course staff your project report and code (for both FindMii and Open project) at cs223bsubmit@gmail.com
March 15th (11:59pm): Code submission deadline for FindMii project
March 16th (11:59pm): Code submission deadline for open project + project report for both project tracks

• FINAL PROJECT PRESENTATION: This will be held on Friday, Mar 18th from 10am to 12pm at Packard Atrium (setup begins at 9am).
This is a mandatory event, so if you have a conflict, please send an email to the staff list. Poster boards and easels will be provided with Stanford ID/driver's license.

•  Midterm grade distribution has been posted.

•  Midterm solutions have been posted.

•  Professor Fei-Fei will be holding additional office hours every Thursday, immediately after lecture, from 10:45am - 11:45am.

•  All assignments have been newly developed to reflect the topics covered in lectures and to prepare students to engage with cutting-edge computer vision literature.


Older Announcements


 

Instructor: Prof. Fei-Fei Li

Office: Room 246 Gates Building

Phone: (650)725-3860

Office hours: Tuesday & Thursday, 10:45am - 11:45am

 

Course Team Email: cs223b-win1011-staff@lists.stanford.edu
Important: Please use the online discussion forum for all questions related to lectures, problem sets or projects. *ONLY* email the Course Team Email when absolutely necessary such as for personal questions. Class/homework/project questions will be answered FASTER if asked on the forum.

 

Not registered through Axess? Sign up to the guest course mailing list to receive latest updates about CS223B: cs223b-win1011-guests

 

Course Assistant:

Rob Cosgriff

Office hours: Wednesday, 11:00am - 12:00pm

Location: B24, Gates Building

 

Dan Goodwin

Office hours: Thursday, 11:00am - 12:00pm

Location: B24, Gates Building

 

Aditya Khosla

Office hours: Thursday, 3:00pm - 4:00pm

Location: B24, Gates Building

 

Andy Lin

Office hours: Monday, 4:00pm - 5:00pm

Location: B26, Gates Building

 

 

Class Time and Location:

Lectures: Tuesday & Thursday, 9:30-10:45am, NVIDIA Auditorium

TA Sections: Friday, 3:15-4:05pm, 191 Skilling Auditorium

 

Course Description:

An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization.

 

Grading Policy:

Problem Sets: 40%
 •  PS0: optional, 0.5% extra credit per problem
 •  PS1: 10%
 •  PS2: 10%
 •  PS3: 10%
 •  PS4: 10%

Midterm exam: 20%

Final project: 40%

Your project will be graded based on 3 major components:
 • Clarity of write-up and presentation (for open project)
 • Technical soundness and innovation
 • Results and Evaluation

Assignment Submission:

All assignments are due by the beginning of class. Please submit your assignments as hardcopy. If you cannot submit in class, write down the date and time of submission, and leave it in the CS223B submission box in the cabinet at the bottom of the Gates A-wing stairwell. It is an honor code violation to write down the wrong time.


SCPD Students: Please submit your assignments via the regular SCPD channels. For more information on how to do this, please refer to this document.


Late policy: Each student will have a total of five free late (calendar) days to use for the assignments. Once these late days are exhausted, any assignments turned in late will be penalized 25% per late day. However, no assignment will be accepted more than three days after its due date. Each 24 hours or part thereof that an assignment is late uses up one full late day. Late days cannot be used for the final project.

Prerequisites

Linear algebra, knowledge of probability and statistics.

 

Textbook:

No required textbooks; Suggested textbooks:
Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010.
Learning OpenCV, by Gary Bradski & Adrian Kaehler, O'Reilly Media, 2008.
Multiple View Geometry in Computer Vision, 2nd Edition, by R. Hartley, and A. Zisserman, Cambridge University Press, 2004.
Computer Vision: A Modern Approach, by D.A. Forsyth and J. Ponce, Prentice Hall, 2002.
Pattern Classification (2nd Edition), by R.O. Duda, P.E. Hart, and D.G. Stork, Wiley-Interscience, 2000.