CS1674: Introduction to Computer Vision, Fall 2016

Location: Sennott Square 5129
Time: Monday and Wednesday, 4:30pm - 5:45pm
Instructor: Adriana Kovashka (email: kovashka AT cs DOT pitt DOT edu; use "CS1674" at the beginning of the subject line)
Office: Sennott Square 5325
Office hours: Monday and Wednesday, 3:30pm - 4:25pm
TA: Yuhuan Jiang (email: yuhuan AT cs DOT pitt DOT edu; use "CS1674" at the beginning of the subject line)
TA's office hours: Monday 2-3pm, Wednesday 2:30-3:30pm, Sennott Square 5422
Additional TAs (office hours only): Chris Thomas and Nils Murrugarra
Additional TAs' office hours: Tuesday 4:30-5:30pm, Sennott Square 5404


Our final exam will be on Wednesday, December 14, 4-5:50pm, in our usual classroom.



Course description: In this class, students will learn the basics of modern computer vision. The first major part of the course will cover fundamental concepts such as image formation, image filtering, edge detection, texture description, feature extraction and matching, and grouping and fitting. A brief intro to machine learning will follow, in preparation for the second course chapter on visual recognition. We will study state of the art approaches in object and scene recognition, attribute-based description, human pose and activity recognition. We will also discuss a few topics from the most recent computer vision conferences, including recognition with deep convolutional neural networks, and interactions between vision and language. The course format will include lectures, written homework assignments, programming homework assignments, and exams.

Prerequisites: CS1501

Textbooks: [top]



Grading will be based on the following components:

Homework Submission Mechanics

You will submit your homework using CourseWeb. Navigate to the CourseWeb page for CS1674, then click on "Assignments" (on the left) and the corresponding homework ID. Your written answers should be a single .pdf/.doc/.docx file. Your code should be a single zip file with .m files (and images/results if requested). Name the file YourFirstName_YourLastName.[extension]. Please comment your code! Homework is due at 11:59pm on the due date. Grades will be posted on CourseWeb.


There will be one in-class midterm exam, and a final exam which will focus on material from the latter part of the course. There will be no make-up exams unless you or a close relative is seriously ill!


Students are expected to regularly attend the class lectures, and should actively engage in in-class discussions. Attendance will not be taken, but keep in mind that if you don't attend, you cannot participate. You can actively participate by, for example, responding to the instructor's or others' questions, asking questions or making meaningful remarks and comments about the lecture, and answering others' questions on Piazza. You are also encouraged to bring in relevant articles you saw in the news.

Late Policy

On your programming assignments only, you get 3 "free" late days, i.e., you can submit your code and results a total of 3 days late. For example, you can submit one programming assignment 12 hours late, and another 60 hours late. Once you've used up your free late days, you will incur a penalty of 25% from the total assignment credit possible for each late day. A late day is anything from 1 minute to 24 hours. Note this policy does not apply to written homework.

Collaboration Policy and Academic Honesty

You will do your work (exams and homework) individually. The work you turn in must be your own work. You are allowed to discuss the assignments with your classmates, but do not look at code they might have written for the assignments, or at their written answers. You are also not allowed to search for code on the internet, use solutions posted online unless you are explicitly allowed to look at those, or to use Matlab's implementation if you are asked to write your own code. When in doubt about what you can or cannot use, ask the instructor! Plagiarism will cause you to fail the class and receive disciplinary penalty. Please consult the University Guidelines on Academic Integrity.

Note on Disabilities

If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services (DRS), 140 William Pitt Union, (412) 648-7890, drsrecep@pitt.edu, (412) 228-5347 for P3 ASL users, as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

Note on Medical Conditions

If you have a medical condition which will prevent you from doing a certain assignment, you must inform the instructor of this before the deadline. You must then submit documentation of your condition within a week of the assignment deadline.

Statement on Classroom Recording

To ensure the free and open discussion of ideas, students may not record classroom lectures, discussion and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student's own private use.



Date Topic Readings Lecture slides Due
8/29 Introduction pptx pdf
8/31 Matlab tutorial Szeliski Sec. 1.1-1.2 pptx pdf
9/5 Labor Day (no class) HW1W
9/7 Matlab tutorial (cont'd); Image filtering Szeliski Sec. 3.2 pptx pdf
9/12 Filtering and texture Szeliski Sec. 3.2, 10.5 pptx pdf HW1P
9/14 Texture representation; Image pyramids Szeliski Sec. 4.1.1 pptx pdf HW2P
9/19 Feature detection Szeliski Sec. 4.1, Grauman/Leibe Ch. 1-3; SIFT paper by David Lowe pptx pdf HW3W
9/21 Feature detection (cont'd) and description [see above] pptx pdf HW3P
9/26 Feature description (cont'd) [see above] [see above] HW4W
9/28 Feature matching and indexing Szeliski Sec. 14.3;
Grauman/Leibe Ch. 4, 6
pptx pdf HW4P
10/3 Affine and projective transformations Szeliski Sec 2.1, 3.6.1, 7.1, 7.2 pptx pdf HW5W
10/5 Epipolar geometry and stereo vision [see above] pptx pdf
10/10 Midterm review pptx pdf HW5P
10/12 Midterm exam
10/17 Fall break (no class)
10/18 Make-up for fall break:
Edge detection, segmentation and grouping
Szeliski Sec. 4.2, 5.3-4 pptx pdf
10/19 Fitting models (Hough transform, RANSAC) Szeliski Sec. 4.3.2;
Grauman/Leibe Ch. 5.2
pptx pdf
10/24 Intro to visual recognition Grauman/Leibe Ch. 7, Sec. 8.1 pptx pdf HW6W
10/26 Scene recognition pptx pdf HW6P
10/31 Support vector machines; Overfitting Bishop PRML Sec. 1.1,
Bishop PRML Sec. 7.1
pptx pdf HW7W
11/2 Attributes and zero-shot learning paper1,
paper2 (skim), paper3 (skim)
pptx pdf HW7P
11/7 Face detection Grauman/Leibe Sec. 8.1, 11.1, Szeliski Sec. 14.1 pptx pdf HW8W
11/9 Deformable part models Szeliski Sec. 14.4, Grauman/Leibe Sec. 8.2, 9, 10.3.3, 11.2,5 pptx pdf HW8P
11/14 Class canceled -- CVPR deadline HW9W
11/16 Neural networks pptx pdf HW9P
11/21 Neural networks (cont'd) Karpathy's notes, Module 1
11/23 Thanksgiving (no class)
11/28 Convolutional neural networks Karpathy's notes, Module 2 pptx pdf HW10W
11/30 Convolutional neural networks (cont'd) HW10P
12/5 Recurrent neural networks;
Tracking, pose and actions
Szeliski Sec. 12.6.4; Pirsiavash and Ramanan, CVPR 2012; Wu et al., CVPR 2016 (optional) pptx pdf
pptx pdf
12/7 Final review pptx pdf HW11P
12/14 Final exam 4pm-5:50pm



This course was inspired by the following courses: Tutorials: Some datasets: [top]