Перевод и анализ слов искусственным интеллектом ChatGPT
На этой странице Вы можете получить подробный анализ слова или словосочетания, произведенный с помощью лучшей на сегодняшний день технологии искусственного интеллекта:
- как употребляется слово
- частота употребления
- используется оно чаще в устной или письменной речи
- варианты перевода слова
- примеры употребления (несколько фраз с переводом)
- этимология
image recognition - перевод на русский
![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\" />")
общая лексика
распознавание изображений
идентификация объектов на введенном в компьютер изображении. Этот процесс начинается с обработки изображения, удаления шума, выделения линий, контуров и областей текстур. Распознавание изображений традиционно относится к задачам искусственного интеллекта
общая лексика
машинное зрение, техническое зрение
направление искусственного интеллекта и обработки изображений, занимающееся обработкой изображений реальной действительности
Смотрите также
математика
опознавание образов
![Disney's [[Magic Kingdom]], near [[Orlando, Florida]], during a trial of a facial recognition technology for park entry](https://commons.wikimedia.org/wiki/Special:FilePath/Disney Facial Recognition 20 April 2021.jpg?width=200 "Disney's [[Magic Kingdom]], near [[Orlando, Florida]], during a trial of a facial recognition technology for park entry")
![Boarding gates with facial recognition technology at [[Beijing West railway station]]](https://commons.wikimedia.org/wiki/Special:FilePath/Entrance faregates at waiting room 10 of Beijing West Railway Station (20190908184801).jpg?width=200 "Boarding gates with facial recognition technology at [[Beijing West railway station]]")
![Automatic face detection with [[OpenCV]]](https://commons.wikimedia.org/wiki/Special:FilePath/Face detection.jpg?width=200 "Automatic face detection with [[OpenCV]]")
![Flight boarding gate with "biometric face scanners" developed by [[U.S. Customs and Border Protection]] at [[Hartsfield–Jackson Atlanta International Airport]]](https://commons.wikimedia.org/wiki/Special:FilePath/Facial recognition technology at gate (44275734970).jpg?width=200 "Flight boarding gate with \"biometric face scanners\" developed by [[U.S. Customs and Border Protection]] at [[Hartsfield–Jackson Atlanta International Airport]]")
![The Viola–Jones algorithm for face detection uses [[Haar-like feature]]s to locate faces in an image. Here a Haar feature that looks similar to the bridge of the nose is applied onto the face.](https://commons.wikimedia.org/wiki/Special:FilePath/Haar Feature that looks similar to the bridge of the nose is applied onto the face.jpg?width=200 "The Viola–Jones algorithm for face detection uses [[Haar-like feature]]s to locate faces in an image. Here a Haar feature that looks similar to the bridge of the nose is applied onto the face.")
![pseudocolor]] image of two people taken in long-wavelength infrared (body-temperature thermal) light](https://commons.wikimedia.org/wiki/Special:FilePath/Ir girl.png?width=200 "pseudocolor]] image of two people taken in long-wavelength infrared (body-temperature thermal) light")
![In this [[shear mapping]] the red arrow changes direction, but the blue arrow does not and is used as eigenvector.](https://commons.wikimedia.org/wiki/Special:FilePath/Mona Lisa eigenvector grid.png?width=200 "In this [[shear mapping]] the red arrow changes direction, but the blue arrow does not and is used as eigenvector.")
общая лексика
распознавание лиц
один из способов биометрической идентификации личности - способность компьютерной системы распознавать людей по изображению лица. Такая система должна различать личность независимо от цвета загара, изменения причёски и т.п.
Смотрите также
![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.")
['pætnrekəg'niʃ(ə)n]
общая лексика
распознавание образов
идентификация графических изображений с помощью компьютерных технологий. Используется, в биометрических методах контроля доступа для распознавания голоса, отпечатков пальцев, фотографий и. п
Определение
Википедия
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.
