Image Analysis and Computer Vision

This course is a broad introduction to Computer Vision. The class is managed by eCommons. Public access to selected items is available here.

Here is the class syllabus:

  • Elements of radiometry and photometry (including color)
  • Image formation, noise models, exposure and HDR
  • Elements of projective geometry
  • Geometric camera model: intrinsic and extrinsic parameters
  • Homography estimation via the Direct Linear Transformation
  • Pose estimation: introduction, special cases
  • Cylindrical projection, panoramic images
  • Epipolar geometry and the 8 point algorithm
  • Stereo depth computation
  • Motion and optical flow
  • Image alignment, Lucas-Kanade algorithm
  • Corner and edge detection
  • Line fitting: least squares, total least squares, RANSAC, Hough transform
  • Invariant features; SIFT descriptors

Instructors and Assistants