Computer Vision

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.
 * 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
 * Machine Learning
 * 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

Video Lectures

 * UCF Computer Vision by Mubarak Shah
 * Image and video processing by Guillermo Sapiro (Youtube )
 * Advanced Vision by Bob Fisher

Lecture Notes

 * CS 143: Introduction to Computer Vision by James Hays
 * CS 223B: Introduction to Computer Vision by Fei-Fei Li
 * CSE576: Computer Vision by Linda Shapiro
 * Introduction to Computer Vision by Robert Collins
 * Computer Vision by John Daugman
 * Computer Vision by Lin Zhang
 * Advances in Computer Vision by MIT

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

 * 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 Artitsts promoting software literacy within the visual arts

Other Resources

 * Computer Vision and Pattern Recognition - Google Scholar Metrics (Top Publications)
 * ComputerVision wikia - Portal on all aspects of Vision and Image Processing
 * Awesome-Computer-Vision (Github) - A curated list of resources
 * Collection of Computer Vision notes - York University
 * Computer Vision on Google+ - online community
 * CVonline - The evolving, distributed, non-proprietary, on-line compendium of Computer Vision
 * Computer VIsion systems in a nutshell
 * Have We Forgotten about Geometry in Computer Vision?