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.
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.