E27: Computer Vision

Spring 2017

Lecture: T/TH 11:20AM-12:35PM (Section 01) or 2:40-3:55PM (Section 02), Hicks 211

Instructor: Matt Zucker


Course description

Computer vision studies how computers can analyze and perceive the world using input from imaging devices. This course introduces topics in computer vision of particular relevance to engineers, with an emphasis on hands-on applications.

Look over the course syllabus for more information.

The topics below are subject to change. As we move through the course, I will update the list to reflect the new schedule, readings, and assignments. Note: some slides and course material courtesy of Martial Hebert, Carnegie Mellon University.


Textbook

Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010-11.

The book is available in its entirety online at http://szeliski.org/Book/. I have not ordered copies at the bookstore, but if you want a hard copy you should be able to find it for purchase online.



Class schedule

Part 1: Image formation and appearance-based methods
Week Dates Topics Reading Labs & HW
1 Jan 17, Jan 19
  • Introduction
  • Linear algebra review
  • Image formation
  • Image representations
  • Homogeneous coordinates
2 Jan 24, Jan 26
  • Lines in 2D
  • Review: ordinary least squares
  • Homographies & homogeneous least squares
  • Thresholding & color segmentation
  • Project 1 briefing
3 Jan 31, Feb 2
  • Morphological operators
  • Filtering & convolution
  • Edge detection
4 Feb 7, Feb 9
  • Frequency domain analysis
  • Template tracking
Part 2: Geometric and 3D methods
5 Feb 14, Feb 16
  • Camera calibration
  • Intrinsic & extrinsic parameters
  • Guest lecture: JPEG image compression (Allan Moser)
6 Feb 21, Feb 23
  • Multiple view geometry
  • Algebraic vs. geometric error
7 Feb 28, Mar 2
  • Stereo
  • Structured light
  • Mar 2 - In-class midterm exam
  • Sections 7-7.1
  • Sections 11-11.3
Spring break
8 Mar 14, Mar 16
  • Singular value decomposition
  • Structure from motion
  • Chapter 7
9 Mar 21, Mar 23
  • Structure from motion, cont'd.
  • Shape from shading
  • Photometric shape from example
Part 3: Recognition and classification
10 Mar 28, Mar 30
  • Section 14.1
11 Apr 4, Apr 6
  • Principal component analysis
  • Eigenfaces
  • Section 14.2
12 Apr 11, Apr 13
  • Clustering & k-means
13 Apr 18, Apr 19
  • AdaBoost
  • Cascade classification
  • Viola-Jones object recognition
14 Apr 25, Apr 27
  • TBA
Finals May 7 Final exam - SCI 101, 2PM-5PM (both class sections)