Computer Vision
Number: CSD 15-385/685
Instructor: Srinivasa Narasimhan
Teaching Assistants: Gunhee Kim
Supreeth Achar
Office Hours:
Srinivasa: By Appointment
Gunhee: Wed. 6pm-8pm
Supreeth: Mon. 6pm-8pm
Time: TR 10.30 am - 11.50 am
Location: WeH 5409 (Lecture)
NSH 3001 (Office Hours)
Bulletin Board: cmu.cs.class.cs385
(Instructions here.)


This course provides a comprehensive introduction to computer vision. The areas that the course will cover are image processing; the physics of image formation; the geometry of computer vision (stereo reconstruction and tracking); and statistical methods for detection and classification.



This course requires familiarity with calculus, basic probability theory and linear algebra. MATLAB, the language of choice for the programming assigments will be covered as part of the introduction to the course. Successful completion of the following courses is required:

15-213/18-243 Introduction to Computer Systems

and either

18-202 Mathematical Foundations of Electrical Engineering

or both

21-241 Matrix Algebra, and
21-259 Calculus in Three Dimensions

Once you've completed 15-385, you may be interested in other courses offered by the Carnegie Mellon Graphics Lab.


The required textbooks for 15-385 this semester is:

Horn, Berthold K.P. Robot Vision. The MIT Press, 1986.

Assignments & Grading

The grades for this course depend on 5 homework assignments and 1 midterm exam. In addition graduate students enrolled in the course will have to complete a final project. The weighting of marks is given below (homework and midterm exam weights will be scaled down to 80% for graduate students with the other 20% for the final project).
  • (15%) Homework 1
  • (15%) Homework 2
  • (20%) Homework 3
  • (10%) Midterm
  • (20%) Homework 4
  • (20%) Homework 5
  • (20%) Final Project (Graduate Students Only)
You will be given a total of three "late days" for the semester. You may use these late days to extend the deadline of any programming or written assignment without penalty. Once all three late days have been used, further extensions will only be granted at the instructor's discretion and may incur a grading penalty.

Note: Please use the Bulletin Board as a primary resource rather than emailing TAs directly. It will yield a more timely respones as all the TAs will be browsing it and your question may even have been answered already!


Note: This syllabus may change during the course. Keep checking back.

Part 0: Introduction
Name: Introduction
Date: Tue 01/17
Slides: Lecture 1 Slides
Name: Working With MATLAB®
Date: Thurs 01/19
Slides: Tutorial (thanks to David Kriegman and Serge Belongie)
More tutorials thanks to Martial Hebert:
Operations, Programming, Working with images
Part 1: Image Processing
Name: 1D Signal Processing
Date: Tue 01/24
Slides: Lecture 3 Slides
Assignment: Assignment 1 Begins
Assignment 1 Handout
Name: Image Processing
Date: Thurs 01/26
Slides: Lecture 4 Slides
Name: Image Pyramids
Date: Tue 01/31
Slides: Lecture 5 Slides
Name: Edge Detection
Date: Thurs 02/02
Slides: Lecture 6 Slides
Name: Hough Transform
Date: Tue 02/07
Slides: Lecture 7 slides
Part 2: Physics Based Vision
Name: Appearance and BRDF
Date: Thurs 02/09
Slides: Lecture 8 Slides
Name: Photometric Stereo
Date: Tue 02/14
Slides: Lecture 9 Slides
Assignment: Assignment 1 Due
Assignment 2 Begins
Assigment 2 Handout
Name: Shape from Shading
Date: Thurs 02/16
Slides: Lecture 10 Slides
Reading: Ramachandran's paper
Name: Direct and Indirect Illumination
Date: Tue 02/21
Reading: CAVE Lab Project Website
Reading: Paper
Part 3: Geometry - Tracking and Reconstruction
Name: Image Formation Geometry and Projection
Date: Thurs 02/23
Slides: Lecture 12 Slides
Name: Optical Flow
Date: Tue 02/28
Slides: Lecture 13 Slides
Name: Image Alignment and Tracking
Date: Thur 03/01
Slides: Lecture 14 Slides (Thanks to Iain Matthews for the slides)
Reading: Lucas-Kanade 20 Years On
Assignment: Assignment 2 Due (3/1 Thursday)
Assignment 3 Starts
Assignment 3 Handout
Name: Midterm Review
Date: Tue 03/06
Name: Midterm Exam
Date: Thurs 03/08
Name: Binocular Stereo 1
Date: Tue 03/20
Slides: Lecture 15 Slides
Name: Binocular Stereo 2
Date: Thur 03/22
Slides: Lecture 16 Slides
Assignment: Assignment 3 Due
Assignment 4 Starts
Assignment 4 Handout
Name: Structured Light Range Imaging
Date: Tue 03/27
Slides: Lecture 17 Slides
Name: Photo-Tourism and Internet Stereo
Date: Thur 03/29
Weblink: Website with results and Google Tech Talk
Part 4: Statistical Methods
Name: Principal Component Analysis 1
Date: Tue 04/03
Slides: Lecture 19 Slides
Name: Principal Component Analysis 2
Date: Thur 04/05
Slides: Lecture 20 Slides
Name: Feature Detection (Blobs and SIFT)
Date: Tue 04/10
Reading: Paper: Distinctive Image Features from Scale-Invariant Keypoints, David Lowe, IJCV 2004.
Name: Feature Detection and Classification
Date: Thur 04/12
Slides: Lecture 22 Slides
Assignment : Assignment 4 Due
Assignment 5 Starts
Assignment 5 Handout
Name: Classification
Date: Tue 04/17
Part 5: Recent Research
Name: Image Based Rendering
Date: Tue 04/24
Slides: Lecture 22 Slides
Notes: Reading
Name: Novel Cameras
Date: Thur 04/26
Slides: Lecture 23: Slides
Name: Open Challenges in Computer Vision
Date: Tue 05/01
Assignment : Assignment 5 Due
Finals and Projects
Name: Graduate Student Project Presentations
Date: Tue 05/08

Last updated: Dec 07, 2011