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Что (кто) такое image recognition - определение
COMPUTERIZED INFORMATION EXTRACTION FROM IMAGES
Computer Vision; Image recognition; Computer vision systems; Image Recognition Techniques; Computational vision; Image understanding; Image Understanding; Image Recognition; Image classification; Computational Vision; Texture recognition; History of computer vision; Visual recognition software; Applications of computer vision; Computer vision intelligence; Computer visual intelligence; Image classifier; Shape recognition; Visual recognition; Image identification; Classification of images; Image-based artificial intelligence; Military applications of computer vision
![Learning 3D shapes has been a challenging task in computer vision. Recent advances in [[deep learning]] have enabled researchers to build models that are able to generate and reconstruct 3D shapes from single or multi-view [[depth map]]s or silhouettes seamlessly and efficiently.<ref name="3DVAE" />](https://commons.wikimedia.org/wiki/Special:FilePath/Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks.png?width=200 "Learning 3D shapes has been a challenging task in computer vision. Recent advances in [[deep learning]] have enabled researchers to build models that are able to generate and reconstruct 3D shapes from single or multi-view [[depth map]]s or silhouettes seamlessly and efficiently.<ref name=\"3DVAE\" />")
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image recognition
<graphics, artificial intelligence> The identification of
objects in an image. This process would probably start with
image processing techniques such as noise removal,
followed by (low-level) feature extraction to locate lines,
regions and possibly areas with certain textures.
The clever bit is to interpret collections of these shapes as
single objects, e.g. cars on a road, boxes on a conveyor belt
or cancerous cells on a microscope slide. One reason this is
an AI problem is that an object can appear very different
when viewed from different angles or under different lighting.
Another problem is deciding what features belong to what
object and which are background or shadows etc. The human
visual system performs these tasks mostly unconsciously but a
computer requires skillful programming and lots of processing
power to approach human performance.
(1997-07-20)
Computer vision
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.
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.
(1994-11-30)
pattern recognition
![The face was automatically detected]] by special software.](https://commons.wikimedia.org/wiki/Special:FilePath/800px-Cool Kids of Death Off Festival p 146-face selected.jpg?width=200 "The face was automatically detected]] by special software.")
BRANCH OF MACHINE LEARNING
Pattern Recognition; Pattern detection; Pattern recognition, visual; Machine pattern recognition; Pattern analysis; Pattern-recognition; Pattern Recognition and Learning; Pattern recognition and learning; Pattern recognition (machine learning); Algorithms for pattern recognition; List of algorithms for pattern recognition; Automated pattern recognition; Automatic pattern recognition; Statistical pattern recognition; Applications of pattern recognition; Fuzzy pattern recognition; List of pattern recognition algorithms
<artificial intelligence, data processing> A branch of
artificial intelligence concerned with the classification or
description of observations.
Pattern recognition aims to classify data (patterns) based
on either a priori knowledge or on statistical information
extracted from the patterns. The patterns to be classified
are usually groups of measurements or observations, defining
points in an appropriate multidimensional space.
A complete pattern recognition system consists of a sensor
that gathers the observations to be classified or described; a
feature extraction mechanism that computes numeric or
symbolic information from the observations; and a
classification or description scheme that does the actual job
of classifying or describing observations, relying on the
extracted features.
The classification or description scheme is usually based on
the availability of a set of patterns that have already been
classified or described. This set of patterns is termed the
training set and the resulting learning strategy is
characterised as supervised. Learning can also be
unsupervised, in the sense that the system is not given an a
priori labelling of patterns, instead it establishes the
classes itself based on the statistical regularities of the
patterns.
The classification or description scheme usually uses one of
the following approaches: statistical (or {decision
theoretic}), syntactic (or structural), or neural.
Statistical pattern recognition is based on statistical
characterisations of patterns, assuming that the patterns are
generated by a probabilistic system. Structural pattern
recognition is based on the structural interrelationships of
features. Neural pattern recognition employs the neural
computing paradigm that has emerged with neural networks.
(1995-09-22)
Pattern recognition
BRANCH OF MACHINE LEARNING
Pattern Recognition; Pattern detection; Pattern recognition, visual; Machine pattern recognition; Pattern analysis; Pattern-recognition; Pattern Recognition and Learning; Pattern recognition and learning; Pattern recognition (machine learning); Algorithms for pattern recognition; List of algorithms for pattern recognition; Automated pattern recognition; Automatic pattern recognition; Statistical pattern recognition; Applications of pattern recognition; Fuzzy pattern recognition; List of pattern recognition algorithms
Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning.
Pattern recognition (psychology)
![[[Whale]], [[submarine]] or [[sheep]]?](https://commons.wikimedia.org/wiki/Special:FilePath/SharkOrSubmarine4024617900.jpg?width=200 "[[Whale]], [[submarine]] or [[sheep]]?")
COGNITIVE PROCESS THAT MATCHES INFORMATION FROM A STIMULUS WITH INFORMATION RETRIEVED FROM MEMORY
Pattern recognition (Physiological Psychology); Top down processing; Top-down processing; Template matching theory; Bottom-up processing; Facial pattern recognition; Music pattern recognition; Visual pattern recognition; Neural mechanisms of facial pattern recognition; Pattern recognition in language acquisition; Recognition (psychology)
In psychology and cognitive neuroscience, pattern recognition describes a cognitive process that matches information from a stimulus with information retrieved from memory.Eysenck, Michael W.
Handwriting recognition
![Method used for exploiting contextual information in the first [[handwritten address interpretation]] system developed by [[Sargur Srihari]] and Jonathan Hull<ref name="Integration of handwritten recognition" />](https://commons.wikimedia.org/wiki/Special:FilePath/Address Recognition.png?width=200 "Method used for exploiting contextual information in the first [[handwritten address interpretation]] system developed by [[Sargur Srihari]] and Jonathan Hull<ref name=\"Integration of handwritten recognition\" />")
ABILITY OF A COMPUTER TO RECEIVE AND INTERPRET INTELLIGIBLE HANDWRITTEN INPUT
Handwriting Recognition; Off-line handwriting recognition; On-line handwriting recognition; Handwriting recognizer; Handwriting recognition system; Handprint Character Recognition; Off-line Handwriting Recognition; On-line Handwriting Recognition; Handwriting OCR; Automated recognition of handwriting; Automatic recognition of handwriting; Automated handwriting recognition; Handwritten text recognition; Computer processing of handwriting; Handwriting identification; Handwriting input device; Online handwriting recognition; Offline handwriting recognition
Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition.
Image viewer
COMPUTER PROGRAM THAT CAN DISPLAY STORED GRAPHICAL IMAGES
Image viewers; Image browser; Image browsing; Image Viewer; Picture viewer
An image viewer or image browser is a computer program that can display stored graphical images; it can often handle various graphics file formats. Such software usually renders the image according to properties of the display such as color depth, display resolution, and color profile.
The Image (1969 film)
1969 FILM BY MICHAEL ARMSTRONG
The Image (short film)
The Image is a 1969 black and white short film directed by Michael Armstrong with starring Michael Byrne and David Bowie in his first film role. The film is one of the few short films ever to receive a certified 'X' Rating and it gained this rating due to its violent content.
Image processor
![video processor]], [[digital signal processor]] (DSP) and a [[32-bit]] [[microcontroller]] controlling the chip](https://commons.wikimedia.org/wiki/Special:FilePath/Nikon D90 - board 0 - Nikon Expeed EI-149-1769.jpg?width=200 "video processor]], [[digital signal processor]] (DSP) and a [[32-bit]] [[microcontroller]] controlling the chip")
SPECIALIZED DIGITAL SIGNAL PROCESSOR USED FOR IMAGE PROCESSING
Image processing engine; Image-processing engine; Image signal processor; Image processing unit
An image processor, also known as an image processing engine, image processing unit (IPU), or image signal processor (ISP), is a type of media processor or specialized digital signal processor (DSP) used for image processing, in digital cameras or other devices.DIGITAL SIGNAL & IMAGE PROCESSINGFundamentals of digital image processing
Википедия
Computer vision
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. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning devices. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.
Sub-domains of computer vision include scene reconstruction, object detection, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration.
Adopting computer vision technology might be painstaking for organizations as there is no single point solution for it. There are very few companies that provide a unified and distributed platform or an Operating System where computer vision applications can be easily deployed and managed.
