Computer vision

visionimage classificationImage recognitionimage understandingartificial visioncomputational visiontexture recognitionclassificationcomputer imagingimage and video recognition
Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos.wikipedia
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3D reconstruction

3D imagingreconstruction3D mapping
Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, and image restoration. Research in projective 3-D reconstructions led to better understanding of camera calibration.
In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects.

Outline of object recognition

object recognitionrecognizing objectsobject
Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, and image restoration.
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence.

Image analysis

imagery analysiscomputer image analysisimagery
Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. The fields most closely related to computer vision are image processing, image analysis and machine vision.
It involves the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital geometry, and signal processing.

Scale space

scale space representationscale-space representationscale-space
These include the concept of scale-space, the inference of shape from various cues such as shading, texture and focus, and contour models known as snakes.
Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities with complementary motivations from physics and biological vision.

Active contour model

active contourActive Contour Modelsactive contour theory
These include the concept of scale-space, the inference of shape from various cues such as shading, texture and focus, and contour models known as snakes.
Active contour model, also called snakes, is a framework in computer vision introduced by Michael Kass, Andrew Witkin and Demetri Terzopoulos for delineating an object outline from a possibly noisy 2D image.

Edge detection

edgeedge detectoredge detectors
Studies in the 1970s formed the early foundations for many of the computer vision algorithms that exist today, including extraction of edges from images, labeling of lines, non-polyhedral and polyhedral modeling, representation of objects as interconnections of smaller structures, optical flow, and motion estimation.
Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction.

Image segmentation

segmentationSegmentation (image processing)segment
At the same time, variations of graph cut were used to solve image segmentation.
In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as image objects).

Graph cuts in computer vision

Graph cutsgraph cutGraph cut segmentation
At the same time, variations of graph cut were used to solve image segmentation.
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision ), such as image smoothing, the stereo correspondence problem, image segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.

Eigenface

eigen trackingeigen-faceeigenfaces
This decade also marked the first time statistical learning techniques were used in practice to recognize faces in images (see Eigenface).
Eigenfaces is the name given to a set of eigenvectors when they are used in the computer vision problem of human face recognition.

Computer graphics (computer science)

computer graphicsgraphics processingcolor graphics
Toward the end of the 1990s, a significant change came about with the increased interaction between the fields of computer graphics and computer vision.

Feature (computer vision)

featuresfeatureimage feature
Recent work has seen the resurgence of feature-based methods, used in conjunction with machine learning techniques and complex optimization frameworks.
In computer vision and image processing, a feature is a piece of information which is relevant for solving the computational task related to a certain application.

Artificial intelligence

AIA.I.artificially intelligent
In the late 1960s, computer vision began at universities which were pioneering artificial intelligence. Areas of artificial intelligence deal with autonomous planning or deliberation for robotic systems to navigate through an environment.
Computer vision is the ability to analyze visual input.

Pattern recognition

pattern analysispattern detectionpatterns
Artificial intelligence and computer vision share other topics such as pattern recognition and learning techniques.
It originated in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition.

Markov random field

Markov networkMarkov NetworksMarkov random fields
Researchers also realized that many of these mathematical concepts could be treated within the same optimization framework as regularization and Markov random fields.
In the domain of artificial intelligence, a Markov random field is used to model various low- to mid-level tasks in image processing and computer vision.

Information engineering (field)

information engineeringInformationIE/Information engineering
Computer vision is often considered to be part of information engineering.
The components of information engineering include more theoretical fields such as machine learning, artificial intelligence, control theory, signal processing, and information theory, and more applied fields such as computer vision, natural language processing, bioinformatics, medical image computing, cheminformatics, autonomous robotics, mobile robotics, and telecommunications.

Deep learning

deep neural networkdeep neural networksdeep-learning
Also, some of the learning-based methods developed within computer vision (e.g. neural net and deep learning based image and feature analysis and classification) have their background in biology.
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

Light field

light fields4D light fieldLight field rendering
This included image-based rendering, image morphing, view interpolation, panoramic image stitching and early light-field rendering.
The plenoptic illumination function is an idealized function used in computer vision and computer graphics to express the image of a scene from any possible viewing position at any viewing angle at any point in time.

Computer graphics

graphicsCGCG artwork
Computer graphics produces image data from 3D models, computer vision often produces 3D models from image data.
Some topics in computer graphics include user interface design, sprite graphics, rendering, ray tracing, geometry processing, computer animation, vector graphics, 3D modeling, shaders, GPU design, implicit surface visualization, image processing, computational photography, scientific visualization, computational geometry and computer vision, among others.

Machine vision

visual navigationcomputer visionvision
The fields most closely related to computer vision are image processing, image analysis and machine vision.
Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science.

Augmented reality

ARaugmentedaugmented reality game
There is also a trend towards a combination of the two disciplines, e.g., as explored in augmented reality.
With the help of advanced AR technologies (e.g. adding computer vision, incorporating AR cameras into smartphone applications and object recognition) the information about the surrounding real world of the user becomes interactive and digitally manipulated.

Artificial neural network

artificial neural networksneural networksneural network
Also, some of the learning-based methods developed within computer vision (e.g. neural net and deep learning based image and feature analysis and classification) have their background in biology.
ANNs have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games, medical diagnosis and even in activities that have traditionally been considered as reserved to humans, like painting.

Robot navigation

robot localizationnavigationlocalization
Areas of artificial intelligence deal with autonomous planning or deliberation for robotic systems to navigate through an environment.

Imaging science

imagingImaging Systemimaging technologies
As an evolving field it includes research and researchers from physics, mathematics, electrical engineering, computer vision, computer science, and perceptual psychology.

Bin picking

Bin picking (also referred to as random bin picking, even sometimes referred to as "The Holy Grail in Sight" ) is a core problem in computer vision and robotics.

Camera resectioning

camera calibrationcalibrationclassic camera calibration
Research in projective 3-D reconstructions led to better understanding of camera calibration.
Camera calibration is often used as an early stage in computer vision.