Editing Pattern recognition (section)

==Uses==
[[File:800px-Cool Kids of Death Off Festival p 146-face selected.jpg|thumb|200px|[[Face recognition|The face was automatically detected]] by special software.]]
Within medical science, pattern recognition is the basis for [[computer-aided diagnosis]] (CAD) systems. CAD describes a procedure that supports the doctor's interpretations and findings. Other typical applications of pattern recognition techniques are automatic [[speech recognition]], [[speaker identification]], [[document classification|classification of text into several categories]] (e.g., spam or non-spam email messages), the [[handwriting recognition|automatic recognition of handwriting]] on postal envelopes, automatic [[image recognition|recognition of images]] of human faces, or handwriting image extraction from medical forms.<ref>{{cite journal|last=Milewski|first=Robert|author2=Govindaraju, Venu|title=Binarization and cleanup of handwritten text from carbon copy medical form images|journal=Pattern Recognition|date=31 March 2008|volume=41|issue=4|pages=1308–1315|doi=10.1016/j.patcog.2007.08.018|bibcode=2008PatRe..41.1308M|url=http://dl.acm.org/citation.cfm?id=1324656|access-date=26 October 2011|archive-date=10 September 2020|archive-url=https://web.archive.org/web/20200910174840/https://dl.acm.org/doi/10.1016/j.patcog.2007.08.018|url-status=live}}</ref><ref>{{cite journal
  |last=Sarangi|first=Susanta |author2=Sahidullah, Md |author3=Saha, Goutam
  |title=Optimization of data-driven filterbank for automatic speaker verification
  |journal=Digital Signal Processing |date=September 2020 |volume=104 
  |page=102795 |doi= 10.1016/j.dsp.2020.102795|arxiv=2007.10729|bibcode=2020DSP...10402795S |s2cid=220665533 }}</ref> The last two examples form the subtopic [[image analysis]] of pattern recognition that deals with digital images as input to pattern recognition systems.<ref name=duda2001>{{cite book|author=[[Richard O. Duda]], [[Peter E. Hart]], [[David G. Stork]]|year=2001|title=Pattern classification|edition=2nd|publisher=Wiley, New York|isbn=978-0-471-05669-0|url=https://books.google.com/books?id=Br33IRC3PkQC|access-date=2019-11-26|archive-date=2020-08-19|archive-url=https://web.archive.org/web/20200819004737/https://books.google.com/books?id=Br33IRC3PkQC|url-status=live}}</ref><ref>R. Brunelli, ''Template Matching Techniques in Computer Vision: Theory and Practice'', Wiley, {{ISBN|978-0-470-51706-2}}, 2009</ref>

Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990.{{citation needed|date=January 2011}} The strokes, speed, relative min, relative max, acceleration and pressure is used to uniquely identify and confirm identity. Banks were first offered this technology, but were content to collect from the FDIC for any bank fraud and did not want to inconvenience customers.{{citation needed|date=January 2011}}

Pattern recognition has many real-world applications in image processing. Some examples include:
* identification and authentication: e.g., [[license plate recognition]],<ref>[http://anpr-tutorial.com/ The Automatic Number Plate Recognition Tutorial] {{Webarchive|url=https://web.archive.org/web/20060820175245/http://www.anpr-tutorial.com/ |date=2006-08-20 }} http://anpr-tutorial.com/ </ref> fingerprint analysis, [[face detection]]/verification,<ref>[https://www.cs.cmu.edu/afs/cs.cmu.edu/usr/mitchell/ftp/faces.html Neural Networks for Face Recognition] {{Webarchive|url=https://web.archive.org/web/20160304065030/http://www.cs.cmu.edu/afs/cs.cmu.edu/usr/mitchell/ftp/faces.html |date=2016-03-04 }} Companion to Chapter 4 of the textbook Machine Learning.</ref> and [[voice-based authentication]].<ref>{{cite journal|last=Poddar|first=Arnab|author2=Sahidullah, Md|author3=Saha, Goutam|title=Speaker Verification with Short Utterances: A Review of Challenges, Trends and Opportunities|journal=IET Biometrics|date=March 2018|volume=7|issue=2|pages=91–101|doi=10.1049/iet-bmt.2017.0065|url=https://ieeexplore.ieee.org/document/8302747|access-date=2019-08-27|archive-date=2019-09-03|archive-url=https://web.archive.org/web/20190903174139/https://ieeexplore.ieee.org/document/8302747/|url-status=dead}}</ref>
* medical diagnosis: e.g., screening for cervical cancer (Papnet),<ref>[http://health-asia.org/papnet-for-cervical-screening/ PAPNET For Cervical Screening] {{webarchive|url=https://archive.today/20120708211332/http://health-asia.org/papnet-for-cervical-screening/ |date=2012-07-08 }}</ref> breast tumors or heart sounds;
* defense: various navigation and guidance systems, [[automatic target recognition|target recognition]] systems, shape recognition technology etc.
* mobility: [[Advanced driver-assistance systems|advanced driver assistance systems]], [[Self-driving car|autonomous vehicle technology]], etc.<ref>{{Cite journal|url=https://saemobilus.sae.org/content/2018-01-0035|title=Development of an Autonomous Vehicle Control&nbsp;Strategy Using a Single Camera and Deep Neural Networks (2018-01-0035 Technical Paper)- SAE Mobilus|website=saemobilus.sae.org|date=3 April 2018 |doi=10.4271/2018-01-0035 |language=en|access-date=2019-09-06|archive-date=2019-09-06|archive-url=https://web.archive.org/web/20190906084436/https://saemobilus.sae.org/content/2018-01-0035|url-status=live}}</ref><ref>{{Cite journal|last1=Gerdes|first1=J. Christian|last2=Kegelman|first2=John C.|last3=Kapania|first3=Nitin R.|last4=Brown|first4=Matthew|last5=Spielberg|first5=Nathan A.|date=2019-03-27|title=Neural network vehicle models for high-performance automated driving|journal=Science Robotics|language=en|volume=4|issue=28|pages=eaaw1975|doi=10.1126/scirobotics.aaw1975|pmid=33137751|s2cid=89616974|issn=2470-9476|doi-access=free}}</ref><ref>{{Cite web|url=https://www.theengineer.co.uk/ai-autonomous-cars/|title=How AI is paving the way for fully autonomous cars|last=Pickering|first=Chris|date=2017-08-15|website=The Engineer|language=en-UK|access-date=2019-09-06|archive-date=2019-09-06|archive-url=https://web.archive.org/web/20190906084433/https://www.theengineer.co.uk/ai-autonomous-cars/|url-status=live}}</ref><ref>{{Cite journal|last1=Ray|first1=Baishakhi|last2=Jana|first2=Suman|last3=Pei|first3=Kexin|last4=Tian|first4=Yuchi|date=2017-08-28|title=DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars|language=en|arxiv=1708.08559|bibcode=2017arXiv170808559T}}</ref><ref>{{Cite journal|last1=Sinha|first1=P. K.|last2=Hadjiiski|first2=L. M.|last3=Mutib|first3=K.|date=1993-04-01|title=Neural Networks in Autonomous Vehicle Control|journal=IFAC Proceedings Volumes|series=1st IFAC International Workshop on Intelligent Autonomous Vehicles, Hampshire, UK, 18–21 April|volume=26|issue=1|pages=335–340|doi=10.1016/S1474-6670(17)49322-0|issn=1474-6670}}</ref>

In psychology, [[pattern recognition (psychology)|pattern recognition]] is used to make sense of and identify objects, and is closely related to perception. This explains how the sensory inputs humans receive are made meaningful. Pattern recognition can be thought of in two different ways. The first concerns template matching and the second concerns feature detection. A template is a pattern used to produce items of the same proportions. The template-matching hypothesis suggests that incoming stimuli are compared with templates in the long-term memory. If there is a match, the stimulus is identified. Feature detection models, such as the Pandemonium system for classifying letters (Selfridge, 1959), suggest that the stimuli are broken down into their component parts for identification. One observation is a capital E having three horizontal lines and one vertical line.<ref>{{cite web |url=http://www.s-cool.co.uk/a-level/psychology/attention/revise-it/pattern-recognition |title=A-level Psychology Attention Revision - Pattern recognition &#124; S-cool, the revision website |publisher=S-cool.co.uk |access-date=2012-09-17 |archive-date=2013-06-22 |archive-url=https://web.archive.org/web/20130622023719/http://www.s-cool.co.uk/a-level/psychology/attention/revise-it/pattern-recognition |url-status=live }}</ref>