computer vision

Also found in: Dictionary, Thesaurus, Medical, Financial, Wikipedia.

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:

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 ?
Our strong computer vision patent portfolio and years of experience place eyeSight in a market leading position" said Gideon Shmuel, CEO of eyeSight Technologies.
Michael Inouye, principal analyst at ABI Research, said that the combination of AI, machine learning, and computer vision will help people use and interact with their devices in new and more profound ways.
The Spanish company, headquartered in Barcelona and with offices in Madrid, Sao Paulo, and Mexico City, was founded in 2002 as a spin-off of the Computer Vision Center of the Universitat Autonoma de Barcelona.
Now, the camera module program is being expanded to include new camera modules capable of utilizing active sensing for superior biometric authentication, and structured light for a variety of computer vision applications that require real-time, dense depth map generation and segmentation.
He completed a PhD in computer vision at Stanford University.
CRISP-ML combines our image processing and computer vision expertise with our advanced machine learning technology to automate and accelerate the holistic tuning of these vision systems, achieving better quality of results at lower cost and much faster than traditional methods.
More work needs to be done to specifically define the processes that cause computer vision syndrome and to develop and improve effective treatments that successfully address these causes.
The second edition adds about 1,000 terms to the 2,500 in the first edition, some new to the fields of computer vision, image processing, and machine vision and some that were skipped over before.
Whether it is cricket ball, basketball or football, it can be hard for fans to see all the action in a fast-paced sports game, which was why Kanade, one of the world's foremost researchers in computer vision, has invented the technology.
Comland is a strong and agile team of experts in computer vision and other IT fields who share a passion for development and innovation.
Programming Computer Vision with Python provides a fine basic review of computer vision in broad terms that any basic programmer can understand, covering a range of topics and including complete code samples with explanations on how to build upon each example.
The malady is computer vision syndrome, and a health care strategy to combat the problem is called 20/20/20: Every 20 minutes, the computer user should take a break for at least 20 seconds and look at objects that are 20 feet away.

Full browser ?