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Line 1: − + + + + + + + + + + + + + + − ==Applications==+ − Computer Vision originally focused on computer systems understanding images well enough to describe what they see, but has grown to systems that give detailed environmental context with more advancements in Machine Learning. Improved Pattern Recognition and [[Convolutional Neural Networks]] methods have given way to new methods for processing images that has application in multiple fields. − ===Optical Character Recognition===+ − [[Google Books]] used scanned images of pages of books to convert those pages to a text format. This allowed users to search the text in the book without needing to read it page by page.+ − ===Medical Imaging===+ − Computer systems use the [[X-Ray]] and [[MRI imaging]] of hospital patients to make a diagnosis as to whether or not they have cancer. In some instances, the computers have outperformed the doctors who make diagnoses on the same patient.https://www.bbc.com/news/health-50857759<ref>{{cite news |last1=Walsh |first1=Fergus |title=AI 'outperforms' doctors diagnosing breast cancer |url=https://www.bbc.com/news/health-50857759 |access-date=24 March 2021 |agency=BBC News |publisher=BBC News |date=January 2 2020}}</ref> − ===Self Driving Cars===+ − Autonomous Cars use a combination of Computer Vision and LiDAR to detect pedestrians and Stop Signs when driving passengers. − + + + + − +
KS update 1.3
'''Computer vision''' is the subfield of Computer Science that studies how a [[computer]] processes visual information to have them interpret what they see. Unlike [[Computer Graphics]] which generates images using computers, Computer Vision takes visual information from the real world and uses algorithms to make predictions and explain what is in the image.
'''Computer vision''' is the subfield of [[Computer science|Computer Science]] that studies how a [[computer]] processes visual information to have them interpret what they see. Unlike [[Computer Graphics]] which generates images using computers, Computer Vision takes visual information from the real world and uses algorithms to make predictions and explain what is in the image.
== Applications ==
Computer Vision originally focused on computer systems understanding images well enough to describe what they see, but has grown to systems that give detailed environmental context with more advancements in [[Machine learning|Machine Learning]]. Improved [[Pattern]] Recognition and [[Convolutional Neural Networks]] methods have given way to new methods for processing images that has application in multiple fields.
== Optical Character Recognition ==
[[Google Books]] used scanned images of pages of books to convert those pages to a text format. This allowed users to search the text in the book without needing to read it page by page.
== Medical Imaging ==
Computer systems use the [[X-Ray]] and [[MRI imaging]] of hospital patients to make a diagnosis as to whether or not they have cancer. In some instances, the computers have outperformed the doctors who make diagnoses on the same patient.https://www.bbc.com/news/health-50857759<ref>{{cite news |last1=Walsh |first1=Fergus |title=AI 'outperforms' doctors diagnosing breast cancer |url=https://www.bbc.com/news/health-50857759 |access-date=24 March 2021 |agency=BBC News |publisher=BBC News |date=January 2, 2020}}</ref>
== Self Driving Cars ==
Autonomous Cars use a combination of Computer Vision and LiDAR to detect pedestrians and Stop Signs when driving passengers.
== Computer Vision Media ==
<gallery widths='160px' heights='100%' mode='traditional' caption=''>
File:Intersection_over_Union_-_object_detection_bounding_boxes.jpg|[[Object detection]] in a photograph
File:Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks.png|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.
File:DARPA Visual Media Reasoning Concept Video.ogv|[[DARPA]]'s Visual Media Reasoning concept video
File:Mars Science Laboratory, 2011-Present.jpg|Artist's concept of ''[[Curiosity (rover)|Curiosity]]'', an example of an uncrewed land-based vehicle. The [[stereo camera]] is mounted on top of the rover.
File:Finger sensor.webp|Rubber artificial skin layer with the flexible structure for the shape estimation of micro-undulation surfaces
File:Silicon Sensor.webp|Above is a silicon mold with a camera inside containing many different point markers. When this sensor is pressed against the surface the silicon deforms and the position of the point markers shifts.
File: SRT Shape Recognition Technology.png|Computer vision for [[people counter]] purposes in public places, malls, shopping centers
==References==
File:LiDAR_Scanner_and_Back_Camera_of_iPad_Pro_2020_-_3.jpg|An [[iPad Pro (4th generation)|2020 model iPad Pro]] with a [[LiDAR]] sensor
</gallery>
== References ==
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[[Category:Computers]]
[[Category:Computer science]]