computer vision

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computer vision

[kəm′pyüd·ər ′vizh·ən]
(computer science)
The use of digital computer techniques to extract, characterize, and interpret information in visual images of a three-dimensional world.

Computer vision

The technology concerned with computational understanding and use of the information present in visual images. In part, computer vision is analogous to the transformation of visual sensation into visual perception in biological vision. For this reason the motivation, objectives, formulation, and methodology of computer vision frequently intersect with knowledge about their counterparts in biological vision. However, the goal of computer vision is primarily to enable engineering systems to model and manipulate the environment by using visual sensing.

Computer vision begins with the acquisition of images. A camera produces a grid of samples of the light received from different directions in the scene. The position within the grid where a scene point is imaged is determined by the perspective transformation. The amount of light recorded by the sensor from a certain scene point depends upon the type of lighting, the reflection characteristics and orientation of the surface being imaged, and the location and spectral sensitivity of the sensor.

One central objective of image interpretation is to infer the three-dimensional (3D) structure of the scene from images that are only two-dimensional (2D). The missing third dimension necessitates that assumptions be made about the scene so that the image information can be extrapolated into a three-dimensional description. The presence in the image of a variety of three-dimensional cues is exploited. The two-dimensional structure of an image or the three-dimensional structure of a scene must be represented so that the structural properties required for various tasks are easily accessible. For example, the hierarchical two-dimensional structure of an image may be represented through a pyramid data structure which records the recursive embedding of the image regions at different scales. Each region's shape and homogeneity characteristics may themselves be suitably coded. Alternatively, the image may be recursively split into parts in some fixed way (for example, into quadrants) until each part is homogeneous. This approach leads to a tree data structure. Analogous to two dimensions, the three-dimensional structures estimated from the imaged-based cues may be used to define three-dimensional representations. The shape of a three-dimensional volume or object may be represented by its three-dimensional axis and the manner in which the cross section about the axis changes along the axis. Analogous to the two-dimensional case, the three-dimensional space may also be recursively divided into octants to obtain a tree description of the occupancy of space by objects.

A second central objective of image interpretation is to recognize the scene contents. Recognition involves identifying an object based on a variety of criteria. It may involve identifying a certain object in the image as one seen before. A simple example is where the object appearance, such as its color and shape, is compared with that of the known, previously seen objects. A more complex example is where the identity of the object depends on whether it can serve a certain function, for example, drinking (to be recognized as a cup) or sitting (to be recognized as a chair). This requires reasoning from the various image attributes and the derivative three-dimensional characteristics to assess if a given object meets the criteria of being a cup or a chair. Recognition, therefore, may require extensive amounts of knowledge representation, reasoning, and information retrieval.

Visual learning is aimed at identifying relationships between the image characteristics and a result based thereupon, such as recognition or a motor action.

In manufacturing, vision-based sensing and interpretation systems help in automatic inspection, such as identification of cracks, holes, and surface roughness; counting of objects; and alignment of parts. Computer vision helps in proper manipulation of an object, for example, in automatic assembly, automatic painting of a car, and automatic welding. Autonomous navigation, used, for example, in delivering material on a cluttered factory floor, has much to gain from vision to improve on the fixed, rigid paths taken by vehicles which follow magnetic tracks prelaid on the floor. Recognition of symptoms, for example, in a chest x-ray, is important for medical diagnosis. Classification of satellite pictures of the Earth's surface to identify vegetation, water, and crop types, is an important function. Automatic visual detection of storm formations and movements of weather patterns is crucial for analyzing the huge amounts of global weather data that constantly pour in from sensors. See Character recognition, Computer graphics, Intelligent machine, Robotics

computer vision

A branch of artificial intelligence and image processing concerned with computer processing of images from the real world. Computer vision typically requires a combination of low level image processing to enhance the image quality (e.g. remove noise, increase contrast) and higher level pattern recognition and image understanding to recognise features present in the image.

Usenet newsgroup: news:comp.ai.vision.

computer vision

Using a computer to capture and analyze images. Also called "machine vision" (MV), there are numerous applications of computer vision, including robotic systems that sense their environment, people detection in surveillance systems, object inspection on an assembly line, image database organization and medical scans. See video content analytics and gesture recognition.
References in periodicals archive ?
Additionally, the researchers found that an area of the brain called the right lateral fusiform gyrus is vital for object recognition.
The future work will be conducted on testing of currently implemented algorithm on a larger number of objects and will include investigations of other methods and algorithms for object recognition.
To satisfy all the properties of recognition, we propose a 3-dimensional object recognition method by using SHOT and relationships of distances and angles in feature points.
The debate regarding the exact nature of the memory process involved in different versions of the object recognition task is becoming increasingly important because of the extensive use of such tasks in studies of memory within the field of behavioural neuroscience (see for example recent reviews by Ameen-Ali & Eacott, 2015; Balser & Heyser, 2015).
The performance of the LBP histogram on texture understanding and object recognition shows that the derivative information can be a powerful way for object representation.
Spontaneous object recognition and object location memory in rats: The effects of lesions in the cingulate cortices, the medial prefrontal cortex, the cingulum bundle and the fornix.
Indoor object recognition system from Indoor scenes are described in this work consist of indoor object detection using AdaBoost classifier algorithm, feature extraction and recognition of indoor object using SVM.
The "Point Cloud Library" (PCL) [4] was used for visualization and object recognition tasks.
Therefore for real time 3D point cloud processing the new 3D point cloud analysis methods should be developed and for this reason the 3D object recognition is most popular topic in recent years.
In one condition, the researchers tested infants' simple object recognition for the target toy by keeping both objects visible, drawing infants' attention to the toys and then placing the toys inside clear containers.
According to the post from VisualGraph founder Kevin Jing, "Our approach is to combine the state-of-the-art machine vision tools, such as object recognition (e.
The "EyeSight" safety system adopts a stereo camera system designed to improve the accuracy of object recognition to avoid collision or reduce collision damage.