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

More specific information is included in each subfield.

Subfields and ConceptsEdit

See Category:Computer Vision for some of its subfields.

  • Low-level Vision
    • Digital Image Processing
    • Feature extraction
      • Hough Transform
    • Feature detection
      • Edge detection
      • Corner detection
    • Optical flow
  • Intermediate-level Vision
    • Recognition tasks
      • Face recognition
    • Object detection
      • Face detection
      • Pedestrian detection
    • Image segmentation
    • 3D reconstruction
    • Motion analysis
  • High-level Vision / Image Understanding
  • Structure from Motion
  • Simultaneous Localization and Mapping (SLAM)
  • Place and Object recognition
    • Object detection
    • Object localization
    • Object classification
    • Scene classification
    • Scene recognition
    • Semantic Scene Understanding
  • Feature descriptors
    • Scale-invariant feature transform (SIFT)
    • Speeded up robust features (SURF)
    • Histogram of oriented gradients (HOG)
  • Medical Image Computing / Medical Image Analysis
  • Computer Graphics
    • Inverse Graphics

Online CoursesEdit

Video LecturesEdit

Lecture NotesEdit



  • 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.


  • 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.


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


  • 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.


See alsoEdit

Other ResourcesEdit