Stitching/Registration: Combining of adjacent 2D or 3D images. Filtering (e.g. morphological filtering) 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. Pixel counting: counts the number of light or dark pixels Segmentation: Partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.Linda G. Shapiro and George C. Stockman (2001): “Computer Vision”, pp 279-325, New Jersey, Prentice-Hall, 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 Edge detection: finding object edges Color Analysis: Identify parts, products and items using color, assess quality from color, and isolate features using color. 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. Neural network / deep learning / machine learning processing: weighted and self-training multi-variable decision making 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. 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. Barcode, Data Matrix and "2D barcode" reading Optical character recognition: automated reading of text such as serial numbers Gauging/Metrology: measurement of object dimensions (e.g. in pixels, inches or millimeters) 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.
stITChiNG/rEgistratiOn: combinING OF AdjAceNt 2d oR 3d ImaGes. FiltEring (e.G. moRpHological FiLtering) 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. pixel CoUNTING: cOunts tHe NuMBEr of liGhT oR dark pixeLs SegmentatioN: partItioning a DigITal IMAgE inTO mulTipLe segmenTs tO siMplIFy And/or Change tHE rEprESentatioN oF An IMAgE InTO sOmetHing that Is More meaningfuL AnD eAsier to aNaLyze.liNda G. shAPirO and george c. sTockman (2001): “ComPuteR visiOn”, Pp 279-325, nEW jersEy, pRenTicE-HaLL, 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 Edge deteCtion: finding ObjEct eDges color AnALysis: iDentIfY parts, ProdUcTs aND IteMs usinG coloR, assesS QuaLiTY from color, anD IsolatE FeaturEs usiNg ColoR. 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. nEural nEtwORk / DeeP leArNinG / macHINe lEarning pRocessing: weiGHTEd and sElF-tRAining MuLtI-variAblE decisIon making 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. 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. barcode, DaTa mAtrix and "2d Barcode" readiNG opTicaL chArActeR recognition: AUtomAtEd readinG oF TEXT SucH As SErIal numBeRs GAuGIng/meTRology: mEaSurEmEnt OF object Dimensions (e.g. in pixels, InChEs or millImEters) 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.