Computer Vision Papers By Topic

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For a motivational overview see What Can We Learn From Computer Vision?

For a human angle, see some of my favorite computer vision researchers.

Here are a few hundred of the most significant computer vision papers, by topic. (Also available as a simple chronological list.)

NB: You may find Google Scholar useful when tracking down references; other useful resources include CiteSeer and arXiv.


  • Level Sets.

  • Linear Programming.

  • Mathematical_Background.

  • Mean-shift, Adaptive Mean Shift, Continuously Adaptive Meanshift ("CamShift"). (Clustering, more robust than old parametric techniques.)

  • Motion, Tracking and Track Analysis

  • New View Synthesis:

  • Nonlinear optimization (compare to Belief Propagation, Graph Cuts Isoperimetric aand Swendsen-Wang):

  • Object Detection and Recognition and Pose Estimation (see also Face Recognition):

  • Occlusion handling:

  • Particle filtering. (Monte Carlo style multihypothesis tracking, regarded as a more robust replacement for Kalman filters.)

  • Random Fields (Note that almost all the Graph Cut papers use Markov Random Fields (MRF) or Conditional Markov Random Fields (CRF) -- most papers do not distinguish the two.)

  • Robust Regression: RANSAC and kith.

  • Semi-Definite Programming.
  • Kuma07b: An Invariant Large Margin Nearest Neighbour Classifier.

  • Semi-Global Matching.
  • Hirs06: Stereo Vision in Structured Environments by Consistent Semi-Global Matching
  • Kuma06: Solving Markov Random Fields using Second Order Cone Programming Relaxations
  • Hirs05: Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information

  • Shadow handling.

  • Smoothing. (See also Particle filtering.)

  • Super Resolution:

  • Swendsen-Wang (compare to Belief Propagation, Graph Cuts and Isoperimetric) propagation, plus be more general.

  • Textures.

  • Thresholding.

  • Video Editing.

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    Last modified: Mon Jan 26 03:28:25 CST 2009