Editing Machine vision (section)

===Image processing===

After an image is acquired, it is processed.<ref name = Davies2nd>{{cite book| author= Davies, E.R. | title=Machine Vision - Theory Algorithms Practicalities | edition=2nd |publisher= Harcourt & Company | isbn=978-0-12-206092-2 | date=1996}}{{Page needed|date=May 2012}}.</ref> Central processing functions are generally done by a [[CPU]], a [[GPU]], a [[FPGA]] or a combination of these.<ref name = PhotonicsSpectra2019/>  Deep learning training and inference impose higher processing performance requirements.<ref name = VSDSept2019>''Finding the optimal hardware for deep learining inference in machine vision'' by Mike Fussell Vision Systems Design magazine September 2019 issue pages 8-9</ref>   Multiple stages of processing are generally used in a sequence that ends up as a desired result. A typical sequence might start with tools such as filters which modify the image, followed by extraction of objects, then extraction (e.g. measurements, reading of codes) of data from those objects, followed by communicating that data, or comparing it against target values to create and communicate "pass/fail" results. Machine vision image processing methods include;

* [[Image stitching|Stitching]]/[[Image registration|Registration]]: Combining of adjacent 2D or 3D images.{{citation needed|date=April 2013}}
* Filtering (e.g. [[Morphological image processing|morphological filtering]])<ref name = "Demant39">{{cite book | author=Demant C.| author2=Streicher-Abel B.| author3=Waszkewitz P.| name-list-style=amp| title=Industrial Image Processing: Visual Quality Control in Manufacturing| publisher=Springer-Verlag | date=1999 | page=39 | isbn=3-540-66410-6}}</ref>
* Thresholding: Thresholding starts with setting or determining a gray value that will be useful for the following steps. The value is then used to separate portions of the image, and sometimes to transform each portion of the image to simply black and white based on whether it is below or above that grayscale value.<ref name = "Demant96">{{cite book | author=Demant C.| author2=Streicher-Abel B.| author3=Waszkewitz P.| name-list-style=amp| title=Industrial Image Processing: Visual Quality Control in Manufacturing| publisher=Springer-Verlag | date=1999 | page=96 | isbn=3-540-66410-6}}</ref> 
* Pixel counting: counts the number of light or dark [[pixel]]s{{citation needed|date=April 2013}}
* [[Segmentation (image processing)|Segmentation]]:  Partitioning a [[digital image]] into multiple [[Image segment|segments]] to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.<ref name="computervision">[[Linda Shapiro|Linda G. Shapiro]] and George C. Stockman (2001):  “Computer Vision”, pp 279-325, New Jersey, Prentice-Hall, {{ISBN|0-13-030796-3}}</ref><ref>Lauren Barghout. Visual Taxometric approach Image Segmentation using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions.  Information Processing and Management of Uncertainty in Knowledge-Based 
Systems. CCIS Springer-Verlag. 2014</ref>
* [[Edge detection]]: finding object edges <ref name = "Demant108">{{cite book | author=Demant C.| author2=Streicher-Abel B.| author3=Waszkewitz P.| name-list-style=amp| title=Industrial Image Processing: Visual Quality Control in Manufacturing| publisher=Springer-Verlag | date=1999 | page=108 | isbn=3-540-66410-6}}</ref>
* Color Analysis: Identify parts, products and items using color, assess quality from color, and isolate [[Feature (computer vision)|features]] using color.<ref name = NASAarticle/>
* [[blob extraction|Blob detection and extraction]]: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks.<ref name = "Demant95">{{cite book | author=Demant C.| author2=Streicher-Abel B.| author3=Waszkewitz P.| name-list-style=amp| title=Industrial Image Processing: Visual Quality Control in Manufacturing| publisher=Springer-Verlag | date=1999 | page=95 | isbn=3-540-66410-6}}</ref> 
* [[Artificial neural network|Neural network]] / [[deep learning]] / [[machine learning]] processing: weighted and self-training multi-variable decision making <ref name ="TurekNeuralNet">{{cite journal | author=Turek, Fred D. |title=Introduction to Neural Net Machine Vision |url= http://www.vision-systems.com/articles/print/volume-12/issue-3/features/introduction-to-neural-net-machine-vision.html |access-date=2013-03-05|journal = Vision Systems Design |date= March 2007 |volume=12|number=3}}</ref>  Circa 2019 there is a large expansion of this, using deep learning and machine learning to significantly expand machine vision capabilities. The most common result of such processing is classification. Examples of classification are object identification,"pass fail" classification of identified objects and OCR.<ref name ="TurekNeuralNet"/>     
* [[Pattern recognition]] including [[template matching]]. Finding, matching, and/or counting specific patterns. This may include location of an object that may be rotated, partially hidden by another object, or varying in size.<ref name = "Demant111">{{cite book | author=Demant C.| author2=Streicher-Abel B.| author3=Waszkewitz P.| name-list-style=amp| title=Industrial Image Processing: Visual Quality Control in Manufacturing| publisher=Springer-Verlag | date=1999 | page=111 | isbn=3-540-66410-6}}</ref> 
* [[Barcode]], [[Data Matrix]] and "[[2D barcode]]" reading  <ref name = "Demant125">{{cite book | author=Demant C.| author2=Streicher-Abel B.| author3=Waszkewitz P.| name-list-style=amp| title=Industrial Image Processing: Visual Quality Control in Manufacturing| publisher=Springer-Verlag | date=1999 | page=125 | isbn=3-540-66410-6}}</ref>  
* [[Optical character recognition]]: automated reading of text such as serial numbers  <ref name = "Demant132">{{cite book | author=Demant C.| author2=Streicher-Abel B.| author3=Waszkewitz P.| name-list-style=amp| title=Industrial Image Processing: Visual Quality Control in Manufacturing| publisher=Springer-Verlag | date=1999 | page=132 | isbn=3-540-66410-6}}</ref> 
* [[Metrology|Gauging/Metrology]]: measurement of object dimensions (e.g. in [[pixel]]s,  [[inch]]es or [[millimeter]]s)   <ref name = "Demant191">{{cite book | author=Demant C.| author2=Streicher-Abel B.| author3=Waszkewitz P.| name-list-style=amp| title=Industrial Image Processing: Visual Quality Control in Manufacturing| publisher=Springer-Verlag | date=1999 | page=191 | isbn=3-540-66410-6}}</ref>
*Comparison against target values to determine a "pass or fail" or "go/no go" result.  For example, with code or bar code verification, the read value is compared to the stored target value.  For gauging, a measurement is compared against the proper value and tolerances. For verification of alpha-numberic codes, the OCR'd value is compared to the proper or target value. For inspection for blemishes, the measured size of the blemishes may be compared to the maximums allowed by quality standards.<ref name="Demant125"/>