Difference between revisions of "Computer Vision"

From Ioannis Kourouklides
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* [http://cvgl.stanford.edu/projects/uav_data/ Stanford Drone Dataset]
* [http://cvgl.stanford.edu/projects/uav_data/ Stanford Drone Dataset]
* [https://deepmind.com/research/open-source/open-source-datasets/kinetics/ Kinetics-400 and Kinetics-600 datasets by DeepMind]
* [https://deepmind.com/research/open-source/open-source-datasets/kinetics/ Kinetics-400 and Kinetics-600 datasets by DeepMind]
* [https://computervisiononline.com/datasets List of datasets by Computer Vision Online]
* [http://www.cvpapers.com/datasets.html CV Datasets on the web]
==See also==
==See also==

Latest revision as of 23:46, 14 January 2019


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[edit]

See Category:Computer Vision for some of its subfields.

  • Image Preprocessing
    • Image Augmentation
  • 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
    • Semantic image segmentation
    • Image registration
    • 3D reconstruction
    • Motion analysis
    • Texture Analysis and Synthesis
      • Co-occurrence Matrix
  • High-level Vision / Image Understanding
  • Structure from Motion
  • Simultaneous Localization and Mapping (SLAM)
  • 3D point clouds
  • Optical Character Recognition (OCR)
  • 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
  • (Combinatorial/Algorithmic) Computational Geometry & Discrete Geometry
  • Computer Graphics
    • Inverse Graphics

Online Courses[edit]

Video Lectures[edit]

Lecture Notes[edit]



  • 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.


  • Boissonnat, J. D., Chazal, F., & Yvinec, M. (2018). Geometric and Topological Inference. Cambridge University Press. (link)
  • 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.
  • Hyvarinen, 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. Springer.



See also[edit]

Other Resources[edit]