E27: Computer Vision

Spring 2016

Lecture: T/TH 11:20AM-12:35PM, 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. We will also learn about state of the art techniques and sensors, including the Microsoft XBOX Kinect sensor.

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.


Useful links


Class schedule

Part 1: Image formation and appearance-based methods
Week Dates Topics Reading Labs & HW
1 Jan 19, Jan 21
  • Introduction
  • Math review
  • Image formation
  • Homogeneous coordinates
2 Jan 26, Jan 28
  • Image representations
  • OpenCV introduction
  • Thresholding & color segmentation
  • Morphological operators
3 Feb 2, Feb 4
  • Filters
  • Convolutions
  • Frequency domain analysis
4 Feb 9, Feb 11
  • Edge detection
  • Hough transform
  • Template tracking
Part 2: Geometric and 3D methods
5 Feb 16, Feb 18
  • Camera calibration
  • Intrinsic & extrinsic parameters
  • Chapter 6
6 Feb 23, Feb 25
  • Exam 1: date and time TBA
  • Multiple view geometry
  • Algebraic vs. geometric error
7 Mar 1, Mar 3
  • Stereo
  • Structured light
  • Sections 7-7.1
  • Sections 11-11.3
Spring break
8 Mar 15, Mar 17
  • Singular value decomposition
  • Structure from motion
  • Chapter 7
9 Mar 22, Mar 24
  • Shape from shading
  • Photometric shape from example
Part 3: Recognition and classification
10 Mar 29, Mar 31
  • Principal component analysis
  • Eigenfaces
11 Apr 5, Apr 7
  • Exam 2: date and time TBA
  • Linear classifiers
12 Apr 12, Apr 14
  • Neural networks
13 Apr 26, Apr 28
  • Clustering & k-means
  • Histogram analysis
  • Texture classification with textons
14 Apr 19, Apr 21
  • AdaBoost
  • Cascade classification
  • Viola-Jones object recognition
Finals TBA