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Outline of computer vision
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The following outline is provided as an overview of and topical guide to computer vision:
Computer vision – interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.[1][2][3] Computer vision tasks include methods for acquiring digital images (through image sensors), image processing, and image analysis, to reach an understanding of digital images. In general, it deals with the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information that the computer can interpret. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images.
Branches of computer vision
[edit]History of computer vision
[edit]Computer vision subsystems
[edit]Image enhancement
[edit]- Image denoising
- Image histogram
- Inpainting
- Super-resolution imaging
- Histogram equalization
- Tone mapping
- Retinex
- Gamma correction
- Anisotropic diffusion (Perona–Malik equation)
Transformations
[edit]- Affine transform
- Homography (computer vision)
- Hough transform
- Radon transform
- Walsh–Hadamard transform
Filtering, Fourier and wavelet transforms and image compression
[edit]Color vision
[edit]- Visual perception
- Human visual system model
- Color matching function
- Color space
- Color appearance model
- Color management system
- Color mapping
- Color model
- Color profile
Feature extraction
[edit]- Active contour
- Blob detection
- Canny edge detector
- Contour detection
- Edge detection
- Edge linking
- Harris Corner Detector
- Histogram of oriented gradients (HOG)
- Random sample consensus (RANSAC)
- Scale-invariant feature transform (SIFT)
Pose estimation
[edit]- Bundle adjustment
- Articulated body pose estimation (BoPoE)
- Direct linear transformation (DLT)
- Epipolar geometry
- Fundamental matrix
- Pinhole camera model
- Projective geometry
- Trifocal tensor
Registration
[edit]- Active appearance model (AAM)
- Cross-correlation
- Geometric hashing
- Graph cut segmentation
- Least squares estimation
- Image pyramid
- Image segmentation
- Level-set method
- Markov random fields
- Medial axis
- Motion field
- Motion vector
- Multispectral imaging
- Normalized cut segmentation
- Optical flow
- Particle filtering
- Scale space
Visual recognition
[edit]Commercial computer vision systems
[edit]Applications
[edit]- 3D reconstruction from multiple images
- Audio-visual speech recognition
- Augmented reality
- Augmented reality-assisted surgery
- Automated optical inspection
- Automatic image annotation
- Automatic number plate recognition
- Automatic target recognition
- Check weigher
- Closed-circuit television
- Computer stereo vision
- Contextual image classification
- DARPA LAGR Program
- Digital video fingerprinting
- Document mosaicing
- Facial recognition systems
- GazoPa
- Geometric feature learning
- Gesture recognition
- Image collection exploration
- Image retrieval
- Image-based modeling and rendering
- Integrated mail processing
- Iris recognition
- Machine vision
- Mobile mapping
- Navigation system components for:
- Object detection
- Optical braille recognition
- Optical character recognition
- Pedestrian detection
- People counter
- Physical computing
- Red light camera
- Remote sensing
- Smart camera
- Traffic enforcement camera
- Traffic sign recognition
- Vehicle infrastructure integration
- Velocity Moments
- Video content analysis
- View synthesis
- Visual sensor network
- Visual Word
- Water remote sensing
Computer vision companies
[edit]Computer vision publications
[edit]Computer vision organizations
[edit]Persons influential in computer vision
[edit]See also
[edit]References
[edit]- ^ Dana H. Ballard; Christopher M. Brown (1982). Computer Vision. Prentice Hall. ISBN 0-13-165316-4.
- ^ Huang, T. (1996-11-19). Vandoni, Carlo E (ed.). Computer Vision : Evolution And Promise (PDF). 19th CERN School of Computing. Geneva: CERN. pp. 21–25. doi:10.5170/CERN-1996-008.21. ISBN 978-9290830955.
- ^ Milan Sonka; Vaclav Hlavac; Roger Boyle (2008). Image Processing, Analysis, and Machine Vision. Thomson. ISBN 978-0-495-08252-1.
External links
[edit]
Computer vision at Wikipedia's sister projects
Definitions from Wiktionary
Media from Commons
News from Wikinews
Quotations from Wikiquote
Texts from Wikisource
Textbooks from Wikibooks
Resources from Wikiversity
- USC Iris computer vision conference list
- Computer vision papers on the web A complete list of papers of the most relevant computer vision conferences.
- Computer Vision Online News, source code, datasets and job offers related to computer vision.
- Keith Price's Annotated Computer Vision Bibliography
- CVonline Bob Fisher's Compendium of Computer Vision.
- British Machine Vision Association Supporting computer vision research within the UK via the BMVC and MIUA conferences, Annals of the BMVA (open-source journal), BMVA Summer School and one-day meetings