Editing Computer vision (section)

===Distinctions===

The fields most closely related to computer vision are [[image processing]], [[image analysis]] and [[machine vision]]. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are similar, something which can be interpreted as there is only one field with different names. On the other hand, it appears to be necessary for research groups, scientific journals, conferences, and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented. In image processing, the input and output are both images, whereas in computer vision, the input is an image or video, and the output could be an enhanced image, an analysis of the image's content, or even a system's behavior based on that analysis.

[[Computer graphics]] produces image data from 3D models, and computer vision often produces 3D models from image data.<ref name="3DVAE">{{Cite book |doi=10.1109/CVPR.2017.269 |last1=Soltani |first1=A. A. |last2=Huang |first2=H. |last3=Wu |first3=J. |last4=Kulkarni |first4=T. D. |last5=Tenenbaum |first5=J. B. |title=2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |chapter=Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks |year=2017 |pages=1511–1519 |hdl=1721.1/126644 |isbn=978-1-5386-0457-1 |s2cid=31373273 |hdl-access=free }}</ref> There is also a trend towards a combination of the two disciplines, ''e.g.'', as explored in [[augmented reality]].

The following characterizations appear relevant but should not be taken as universally accepted:

* [[Image processing]] and [[image analysis]] tend to focus on 2D images, how to transform one image to another, ''e.g.'', by pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or geometrical transformations such as rotating the image. This characterization implies that image processing/analysis neither requires assumptions nor produces interpretations about the image content.
* Computer vision includes 3D analysis from 2D images. This analyzes the 3D scene projected onto one or several images, ''e.g.'', how to reconstruct structure or other information about the 3D scene from one or several images. Computer vision often relies on more or less complex assumptions about the scene depicted in an image.
* [[Machine vision]] is the process of applying a range of technologies and methods to provide imaging-based automatic inspection, process control, and robot guidance<ref name="NASAarticle"/> in industrial applications.<ref name="TextbookP1"/> Machine vision tends to focus on applications, mainly in manufacturing, ''e.g.'', vision-based robots and systems for vision-based inspection, measurement, or picking (such as [[bin picking]]<ref>{{Cite web | url=https://www.robots.com/blog/viewing/the-future-of-automated-random-bin-picking | title=The Future of Automated Random Bin Picking | access-date=2018-01-10 | archive-date=2018-01-11 | archive-url=https://web.archive.org/web/20180111164947/https://www.robots.com/blog/viewing/the-future-of-automated-random-bin-picking | url-status=live }}</ref>). This implies that image sensor technologies and control theory often are integrated with the processing of image data to control a robot and that real-time processing is emphasized by means of efficient implementations in hardware and software. It also implies that external conditions such as lighting can be and are often more controlled in machine vision than they are in general computer vision, which can enable the use of different algorithms.
* There is also a field called [[imaging science|imaging]] which primarily focuses on the process of producing images, but sometimes also deals with the processing and analysis of images. For example, [[medical imaging]] includes substantial work on the analysis of image data in medical applications. Progress in [[Convolutional neural network|convolutional neural networks]] (CNNs) has improved the accurate detection of disease in medical images, particularly in cardiology, pathology, dermatology, and radiology.<ref>{{Cite journal |last1=Esteva |first1=Andre |last2=Chou |first2=Katherine |last3=Yeung |first3=Serena |last4=Naik |first4=Nikhil |last5=Madani |first5=Ali |last6=Mottaghi |first6=Ali |last7=Liu |first7=Yun |last8=Topol |first8=Eric |last9=Dean |first9=Jeff |last10=Socher |first10=Richard |date=2021-01-08 |title=Deep learning-enabled medical computer vision |journal=npj Digital Medicine |volume=4 |issue=1 |page=5 |doi=10.1038/s41746-020-00376-2 |pmid=33420381 |issn=2398-6352|pmc=7794558 }}</ref>
* Finally, [[pattern recognition]] is a field that uses various methods to extract information from signals in general, mainly based on statistical approaches and [[artificial neural networks]].<ref>{{Cite journal|last1=Chervyakov|first1=N. I.|last2=Lyakhov|first2=P. A.|last3=Deryabin|first3=M. A.|last4=Nagornov|first4=N. N.|last5=Valueva|first5=M. V.|last6=Valuev|first6=G. V.|year=2020|title=Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network|url=|journal=Neurocomputing|volume=407|pages=439–453|doi=10.1016/j.neucom.2020.04.018|s2cid=219470398|quote=Convolutional neural networks (CNNs) represent deep learning architectures that are currently used in a wide range of applications, including computer vision, speech recognition, identification of albuminous sequences in bioinformatics, production control, time series analysis in finance, and many others.}}</ref> A significant part of this field is devoted to applying these methods to image data.

[[Photogrammetry]] also overlaps with computer vision, e.g., [[stereophotogrammetry]] vs. [[computer stereo vision]].