Textbook

The course textbook is:

    Computer Vision: Algorithms and Applications, by Rick Szeliski.

It is freely available online or may be purchased in hardcopy.  Course lecture slides will be posted below and are also a useful reference.

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

	Date	Topic	Readings and links	Lectures	Assignments
	Tues Jan 17	Course intro	Textbook Sec 1.1-1.3 Course requirements UTCS account setup Basic Matlab tutorial Running Matlab at UT	slides	A0 out See optional Latex info
	Thurs Jan 19	Features and filters	Sec 3.1.1-2, 3.2	Linear filters slides handout slides ppt
	Tues Jan 24		Sec 3.2.3, 4.2 Seam carving paper Seam carving video	Gradients and edges slides handout slides ppt	A0 due Friday Jan 27 A1 out See optional Latex info
	Thurs Jan 26		Sec 3.3.2-4	Binary image analysis slides handout slides ppt
	Tues Jan 31		Sec 10.5 Texture Synthesis Texture synthesis by non-parametric sampling, Efros & Leung Video textures Style transfer for video	Texture slides handout slides ppt
	Thurs Feb 2		Sec 8.4 (up until 8.4.1) Motion magnification	Optical flow slides handout slides ppt	A1 due Friday Feb 3
	Tues Feb 7	Grouping and Fitting	Sec 5.2-5.4	Segmentation and clustering algorithms slides handout slides ppt
	Thurs Feb 9		Sec 5.5 and skim papers below Predicting Sufficient Annotation Strength for Interactive Segmentation, Jain and Grauman, ICCV 2013 [web] Click Carving: Segmenting Objects in Video with Point Clicks, Jain and Grauman, HCOMP 2016 [videos] Multi-Scale Combinatorial Grouping, Arbelaez et al. CVPR 2014	Interactive segmentation Guest lecture: Suyog Jain slides handout slides ppt	A2 out
	Tues Feb 14		Sec 4.3.2 Hough Transform line demo Hough Transform video Hough Transform circle demo	Clustering wrap-up and Hough transform, part 1 slides handout slides ppt
	Thurs Feb 16			Hough transform, part 2 slides handout slides ppt
	Tues Feb 21		Sec 5.1.1	Deformable contours slides handout slides ppt
	Thurs Feb 23	Multiple views	Sec 4.1	Local invariant features: detection slides handout slides ppt	A2 due Friday Feb 24
	Tues Feb 28			Local invariant features: description and matching slides handout slides ppt
	Thurs Mar 2		Sec 2.1.1, 2.1.2, 6.1.1, 6.1.4 RANSAC song	Alignment slides handout slides ppt
	Tues Mar 7		Sec 3.6.1 HP frames video 1 HP frames video 2	Homography and image warping slides handout slides ppt
	Thurs Mar 9			Midterm exam in class 1 sheet of notes is allowed	A3 out
	Tues Mar 21		Sec 11.1.1, 11.2-11.5	Image warping, stereo part 1 slides handout slides ppt 11 am faculty candidate talk in vision: Carl Vondrick, MIT, GDC auditorium
	Thurs Mar 23		Sec 11.1.1, 11.2-11.5 Audio camera	Stereo, part 1 slides handout slides ppt
	Thurs Mar 30		Graph cuts stereo matching demo Middlebury stereo database DeepStereo, Flynn et al. video Object labeling in RGB-D videos, Lai et al.	Stereo, part 2 slides handout slides ppt
	Tues April 4	Recognition and learning	Synthesis Ch 4, 5, 6 (pdf on Canvas) Szeliski 14.3 Video Google demo by Sivic et al., paper David Lowe's SIFT and Generalized Hough approach (Lowe, IJCV 2004)	Instance recognition 11 am faculty candidate talk in vision: Phillip Isola, UC Berkeley, GDC auditorium slides handout slides ppt	A4 out
			Stanford Mobile Visual Search Data Set, Chandrasekhar et al.	Instance recognition part 2 slides handout slides ppt
				Mining for objects Intro to categories slides handout slides ppt
			Viola-Jones face detection paper	Discriminative classifiers Face detection slides handout slides ppt
			Burges SVM tutorial Dalal-Triggs pedestrian detection paper Hays-Efros im2gps paper	Discriminative classifiers slides handout slides ppt	A5 out
			Vondrick et al. Hoggles paper	Support vector machines, kernels, HOG slides handout slides ppt
			Grauman-Darrell Pyramid match kernel paper Lazebnik et al. Spatial pyramids paper	Pyramid match kernels, Neural networks intro slides handout slides ppt
			Convolutional neural networks for visual recognition (Stanford) Krizhevsky et al. Imagenet classification with deep convolutional neural networks paper Clarifai demo	Convolutional neural networks slides handout slides ppt
			Relative attributes Visual recognition with humans in the loop Fine-grained visual comparisons with local learning UT Zappos 50K Dataset WhittleSearch project Visipedia project Merlin bird app	Attributes slides handout slides ppt
				Course wrap-up A5 leaderboard

Assignments:  Assignments will be given approximately every two weeks.  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.  (See Section II of the UTCS Code of Conduct regarding attendance expectations.) 

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, tablet, etc. during class.
• Please read and follow the UTCS code of conduct.

Important Dates

Midterm exam: Thursday Mar 9 (in class, date tentative)
Last class meeting: Thursday May 4
Final exam:  Tuesday May 16, 9-12 noon in GDC 1.304 (as set by registrar).  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 Fri Jan 27
  
• A1 due Fri Feb 3
  
• A2 due Fri Feb 24 (tentative)
• A3 due Fri Mar 31 (tentative)
• A4 due Fri April 14 (tentative)
• A5 due Tues May 2 (tentative)

Grading Policy

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

• Assignments (50%, equally weighted for A1-5; 1 point for A0)
• Midterm exam (15%)
• Final exam (25%)
• Class participation, including attendance (10%)

Academic Dishonesty Policy

You are encouraged to discuss the readings and concepts with classmates. However, all written work and code must be your own. 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.  The case will also be reported to the Office of the Dean of Students, which may institute its own disciplinary measures. 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.

Latex Templates for Assignment Write-ups (Optional)

You may use any tool for preparing assignment write-ups that you like, so long as it is organized and clear.  Typically we ask for a mix of descriptions/explanations as well as embedded figures composed of images and/or plots produced in Matlab.

Below we provide some info about using Overleaf, a free online editor for Latex.  Overleaf provides various Latex templates and compiles your edited .tex files into a pdf automatically.  The basics:

    1) go to overleaf.com
    2) sign up/sign in
    3) click new project on the left
    4) scroll down to "Homework Assignment" and click on "more homework assignment templates"
    5) choose whichever template you feel comfortable with and click "open as template"
    6) start editing
    7) once you are done editing, click "PDF" in the panel above. A pdf file will be generated and downloaded automatically.

Here are instructions about inserting images.
How to position images.
Captioning, scaling, resizing.