Difference between revisions of "Computer Vision"

From Ioannis Kourouklides
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* Structure from Motion
 
* Structure from Motion
 
* [[Simultaneous Localization and Mapping]] (SLAM)
 
* [[Simultaneous Localization and Mapping]] (SLAM)
 +
* Place and Object recognition
 
* Feature descriptors
 
* Feature descriptors
 
** Scale-invariant feature transform (SIFT)
 
** Scale-invariant feature transform (SIFT)

Revision as of 17:31, 25 April 2018

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This page contains resources about Computer Vision, Machine Vision and Image Processing in general.

More specific information is included in each subfield.

Subfields and Concepts

See Category:Computer Vision for some of its subfields.

Online Courses

Video Lectures


Lecture Notes

Books

Practical

  • Howse, J. (2013). OpenCV Computer Vision with Python. Packt Publishing Ltd.
  • Demaagd, K., Oliver, A., Oostendorp, N., & Scott, K. (2012). Practical Computer Vision with SimpleCV: The Simple Way to Make Technology See. O'Reilly Media, Inc.
  • Solem, J. E. (2012). Programming Computer Vision with Python: Tools and algorithms for analyzing images. O'Reilly Media, Inc.
  • Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, Inc.

Introductory

  • Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer.
  • Zisserman, A., & Hartley, R. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press.
  • Forsyth, D. A., & Ponce, J. (2002). Computer Vision: A Modern Approach. Prentice Hall.

Advanced

  • Jahne, B., Geissler, P., & Haussecker, H. (1999). Handbook of Computer Vision and Applications with CD-ROM. Morgan Kaufmann Publishers Inc.

Specialized

  • Prince, S. J. D. (2012). Computer Vision: Models, Learning, and Inference. Cambridge University Press.
  • Nowozin, S., & Lampert, C. H. (2011). Structured Prediction and Learning in Computer Vision. Foundations and Trends in Computer Graphics and Vision, 6(3-4), 3-4.
  • Hyvärinen, A., Hurri, J. & Hoyer, P. O. (2009). Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. Springer.
  • Ma, Y. (Ed.). (2004). An Invitation to 3D Vision: From Images to Geometric Models (Vol. 26). Springer.

Software

See also

Other Resources