Resources

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  • D. A. Forsyth and J. Ponce, Computer Vision — A Modern Approach, second edition, Pearson 2012
  • R. Szeliski, Computer Vision — Algorithms and Applications, Springer 2011
  • R. Klette, Concise Computer Vision — An Introduction into Theory and Algorithms, Springer 2014
  • S. J. D. Prince, Computer Vision — Models, Learning, and Inference, Cambridge 2012
  • R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge 2000
  • Y. Ma, S. Soatto, J. Košecká, S. S. Sastry, An Invitation to 3-D Vision — From Images to Geometric Models, Springer 2004

  • C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2006
  • T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning — Data Mining, Inference, and Prediction, Springer 2001
  • K. P. Murphy, Machine Learning — A Probabilistic Perspective, MIT Press 2012
  • S. J. Dickinson, A. Leonardis, B. Schiele, and M. J. Tarr, editors, Object Categorization — Computer and Human Vision Perspectives, Cambridge 2009

  • LaTeX installation instructions for several platforms can be found in the class notes on LaTeX, as well as in Appendix A of George Grätzer's book More Math Into LaTeX, which is also a good text to read when you delve deeper into LaTeX.
  • Movie tutorials on LaTeX can be found in this zip file.
  • A quick LaTeX tutorial can be found here, and the quick start version on the same site is an even faster, one-page introduction, and is required reading.
  • The LaTeX Wikibook is an authoritative online reference to LaTeX.

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