Difference between revisions of "Computer Vision"

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.

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
- Machine Learning
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

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.

OpenCV - C++, C, Python and Java interfaces that support Windows, Linux, Mac OS, iOS and Android
SimpleCV - Framework using Python
OpenNI - Official website was shut down by Apple Inc.
OpenBR - Open Source Biometric Recognition
PCL - Point Cloud Library
ICL - Image Component Library
Image Processing Toolbox - MATLAB
Computer Vision System Toolbox- MATLAB
VXL - C++
Processing - IDE for Computational Artists promoting software literacy within the visual arts

@@ Line 106: / Line 106: @@
 * [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://computervisiononline.com/datasets List of datasets by Computer Vision Online]
+* [http://www.cvpapers.com/datasets.html CV Datasets on the web]
 ==See also==