Instructor Spring 2019: Ioana Fleming

Texts

  • Ponce and Forsyth – Computer Vision: A Modern Approach, 4th edition (3rd edition ok as well)
  • 2. Richard Szelisky – Computer Vision: Algorithms and Applications http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf
  • Note: Research publications outlining several well-known computer vision algorithms will be distributed via Moodle.
  • Software: Matlab (various versions) is installed on campus lab computers. A list of labs, with the associated environments and matlkab version has been posted on Moodle. The latest release is 2018a. Instructions for downloading Matlab on your native machines is posted on Moodle as well.
  • Course materials, such as lecture notes and assignments, will be available in electronic form on the Moodle site for the course: http://moodle.cs.colorado.edu/

Topics:

  • Linea Algebra Review
  • Image Transformations. Homogenous coordinates. Projective geometry
  • Camera parameters. Camera Calibration
  • Image Filters
  • Edge Detection. Corner Detection. Fitting
  • Feature Detection and Matching. Homographies. Image Stitching/Mosaicing
  • Epipolar Geometry. Stereo Vision
  • Segmentation, Recognition and Classification
  • Dynamic Programming
  • Face Detection
  • Motion and Tracking. Optical Flow
  • Classifiers. SVMs, Neural Nets
  • Deep Learning – as time permits

Grading Policy

  • Assignments (5-6): 80%
  • Project: 20%
  • Grading Interviews: Homework assignments will be graded through an interview with the TA. Your TA will announce when grading meetings are available, and it is your responsibility to schedule an interview with your TA as soon as the scheduler is posted on Moodle. Your TA will ask you questions about the work you will have submitted the previous week. These questions are designed to test your understanding of the solution code submitted.