Basic knowledge of probability and linear algebra; data structures, algorithms; programming experience.  Previous experience with image processing will be useful but is not assumed. 

Assignments will consist largely of Matlab programming problems.  There will be a warm-up assignment to get familiar with basic Matlab commands.  We will recommend useful functions to check out per assignment.  However, students are expected to practice and pick up Matlab on their own in order to complete the assignments.  The instructor and TA are happy to help with Matlab issues during office hours. 

If you are unsure if your background is a good match for this course, please come talk to the instructor.

Assignments:  Assignments will be given approximately every two weeks, and will involve a combination of short-answer questions and programming problems.  The programming problems will provide hands-on experience working with techniques covered in or related to the lectures.  All code and written responses must be completed individually.  Most assignments will take significant time to complete.  Please start early, and use Piazza and/or see us during office hours for help if needed.    Please follow instructions in each assignment carefully regarding what to submit and how to submit it.

Extension policy: If you turn in your assignment late, expect points to be deducted. Extensions will be considered on a case-by-case basis, but in most cases they will not be granted.  The greater the advance notice of a need for an extension, the greater the likelihood of leniency.  For programming assignments, by default, 10 points (out of 100) will be deducted for lateness for each day late.  We will use the submission program timestamp to determine time of submission.  One day late = from 1 minute to 24 hours past the deadline.  Two days late = from 24 hours and 1 minute to 48 hours past the deadline.  We will not accept assignments more than 4 days late, or once solutions have been discussed in class, whichever is sooner.

Exams:  There is an in-class midterm and a comprehensive final exam. 

Participation/attendance:  Regular attendance is expected.  If for whatever reason you are absent, it is your responsibility to find out what you missed that day.  Note that attendance does factor into the final grade. 

General responsibilities:  Beyond the above, your responsibilities in the class are:

• Come to lecture on time.
• Check the class webpage for assignment files, notes, announcements etc.
• Use Piazza for class-related discussion and assignment help (no spoilers, please!)
  
• Complete the readings prior to lecture.  The reading assignments listed on the schedule should be read before the associated class lecture.
• Please do not use a laptop, cell phone, etc. during class.
• Please read and follow the UTCS code of conduct.

Grades will be determined as follows.  You can check your current grades online using Canvas.

• Assignments (50%)
• Midterm exam (15%)
• Final exam (25%)
• Class participation, including attendance (10%)

Midterm exam: Thursday, Oct 22 (in class, date tentative)
Last class meeting: Thursday, Dec 3
Final exam:  Wednesday, Dec 9, 2-5 pm.  Location JGB 2.216.  The exam is given during the normal final exam period and will be offered at that time only.

Assignments are due about every two weeks.  The dates below are tentative and are provided to help your planning.  They are subject to minor shifts if the lecture plan needs to be adjusted slightly according to our pace in class. 

• A0 due Sept 4
  
• A1 due Sept 18 (tentative)
• A2 due Oct 9 (tentative)
• A3 due Oct 29 (tentative)
• A4 due Nov 13 (tentative)
• A5 due Dec 1 (tentative)
  

The recommended textbook is freely available online or may be purchased


You may also find the following books useful.

• Computer Vision: A Modern Approach, David A. Forsyth and Jean Ponce
• Computer Vision, Linda G. Shapiro and George C. Stockman
• Introductory Techniques for 3-D Computer Vision, Emanuele Trucco and Alessandro Verri.
• Multiple View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman.
• Pattern classification, Richard O. Duda, Peter E. Hart, and David G. Stork
• Pattern Recognition and Machine Learning.  Christopher M. Bishop
• Visual Object Recognition.  K. Grauman and B. Leibe
  

Academic dishonesty policy

You are encouraged to discuss the readings and concepts with classmates. However, all written work and code must be your own. And programming assignments must be your own, except for 2-person teams when teams are authorized.   If we do not explicitly authorize 2-person teams for an assignment, you can assume they are not permitted for that assignment.  All work ideas, quotes, and code fragments that originate from elsewhere must be cited according to standard academic practice. Students caught cheating will automatically fail the course. If in doubt, look at the departmental guidelines and/or ask.


Notice about students with disabilities

The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. To determine if you qualify, please contact the Dean of Students at 471-6529; 471-4641 TTY. If they certify your needs, I will work with you to make appropriate arrangements.


Notice about missed work due to religious holy days

A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.