DigitalImageProcessing2-ImageProcessingFundamentals.ppt

coursewaredownload ftp username downloadpassword download digitalimageprocessing digitalimagingfundamentals dr guangmingluluguangm contents mainpurpose introduceseveralconceptsrelatedtodipandsomeofthenotationusedthroughoutthecourse thislecturewillcover humanvisionsystemlightandelectromagneticspectrumimageacquisitionsamplingandquantizationresolutionbasicrelationshipsbetweenpixels humanvisualsystem thebestvisionmodelwehave knowledgeofhowimagesformintheeyecanhelpuswithprocessingdigitalimageswewilltakejustawhirlwindtourofthehumanvisualsystem structureofthehumaneye thelensfocuseslightfromobjectsontotheretinatheretinaiscoveredwithlightreceptorscalledcones 6 7million androds 75 150million conesareconcentratedaroundthefoveaandareverysensitivetocolourrodsaremorespreadoutandaresensitivetolowlevelsofillumination structureofthehumaneye blind spotexperiment drawanimagesimilartothatbelowonapieceofpaper thedotandcrossareabout6inchesapart closeyourrighteyeandfocusonthecrosswithyourlefteyeholdtheimageabout20inchesawayfromyourfaceandmoveitslowlytowardsyouthedotshoulddisappear brightnessadaptation discrimination becausedigitalimagesaredisplayedasadiscretesetofintensities theeye sabilitytodiscriminatebetweendifferentintensitylevelsisanimportantconsiderationindipresults thehumanvisualsystemcanperceiveapproximately1010differentlightintensitylevelssubjectivebrightnessisalogarithmfunctionofthelightintensityincidentontheeye however atanyonetimewecanonlydiscriminatebetweenamuchsmallernumber brightnessadaptation foragivensetofconditions thecurrentsensitivitylevelofthevisualsystemiscalledthebrightnessadaptationlevel brightnessadaptation discrimination brightnessadaptation discrimination cont perceivedbrightnessisnotasimplefunctionofintensity machbands 1865 seeingisbelieving brightnessadaptation discrimination cont similarly theperceivedintensityofaregionisrelatedtothelightintensitiesoftheregionssurroundingit brightnessadaptation discrimination cont anotherexperiment apieceofpaperonadeskisalwayswhite butcanappeartotallyblackwhenusedtoshieldtheeyeswhilelookingdirectlyatabrightsky opticalillusions ourvisualsystemsplaylotsofinterestingtricksonus opticalillusions cont lightandtheelectromagneticspectrum newton 1666violet blue green yellow orange andred blendssmoothlyintothenext lightisjustaparticularpartoftheelectromagneticspectrumthatcanbesensedbythehumaneye lightandtheelectromagneticspectrum lightandtheelectromagneticspectrum visiblelight 0 43 0 79umtheelectromagneticspectrumissplitupaccordingtothewavelengthsofdifferentformsofenergywherecisthespeedofthelight visthefrequency andhistheplanck sconstant lightandtheelectromagneticspectrum astreamofmasslessparticleseachmasslessparticlecontainsacertainamountofenergy eachbundleofenergyiscalledaphotonhighfrequencyelectromagneticphenomenacarrymoreenergyperphoton thatisthereasonthatgammaraysaresodangeroustolivingorganisms lightandtheelectromagneticspectrum thecoloursthatweperceivearedeterminedbythenatureofthelightreflectedfromanobjectforexample ifwhitelightisshoneontoagreenobjectmostwavelengthsareabsorbed whilegreenlightisreflectedfromtheobject whitelight coloursabsorbed greenlight lightreflectanceproperties abodythatreflectslightandisrelativelybalancedinallvisiblewavelengthsappearswhitetotheobserver abodythatfavorsreflectanceinalimitedrangeofthevisiblespectrumexhibitssomeshadesofcolor achromaticormonochromaticlight theonlyattributeisintensity gray levelblacktograytowhite lightandtheelectromagneticspectrum chromaticlight threebasicquantitiestodescribethequalityofachromaticlightsource radiance watts w 发光强度 thetotalamountofenergythatflowsfromthelightsource luminance lumen lm 光通量 ameasureoftheamountofenergyanobserverperceivesfromalightsource example farinfraredregionbrightnesssubjectivedescriptoroflightperceptionthatispracticallyimpossibletomeasure lightandtheelectromagneticspectrum lightandtheelectromagneticspectrum inprinciple ifasensorcanbedevelopedthatiscapableofdetectingenergyradiatedbyabandoftheelectromagneticspectrum wecanimageeventsofinterestinthatband however thewavelengthofanelectromagneticwaverequireto see anobjectmustbeofthesamesizeasorsmallerthantheobject electromagneticwavesisnottheonlymethodforimagegeneration suchassoundreflection ultrasonicimagesnotethereisanerrorinthereferencebookinthissection farinfraredshouldbefarultraviolet page35 otheremspectrum short wavelengthend gammaraysmedicalimagingastronomicalimagingradiationinnuclearenvironmentshardxraysindustrialapplicationssoftxrayschestx ray shorterwavelengthend dentalx ray lowerenergyend ultravioletmicroscopyimaginginfraredregion near infraredfar infraredmicrowavemicrowaveovens communication radarradiowaveam fm tv andmedicalimagingstellarbodies observation lightandtheelectromagneticspectrum imageacquisition imagesaretypicallygeneratedbyilluminatingasceneandabsorbingtheenergyreflectedbytheobjectsinthatscene imagingsensors imageacquisitionsensorssinglesensorstripsensorsensorarray incomingenergylandsonasensormaterialresponsivetothattypeofenergyandthisgeneratesacontinuousvoltagetocreateadigitalimage weneedtoconvertthecontinuoussenseddataintodigitalform thisinvolvestwosteps samplingandquantization imagesamplingandquantization imagesamplingandquantization imagesamplingandquantization digitizingthecoordinatevaluesiscalledsampling anddigitizingtheamplitudevaluesiscalledquantization quantisationistheprocessofconvertingacontinuousanaloguesignalintoadigitalrepresentationofthissignal mathematicalstatement letzbethesetofrealintegersrthesetofrealnumberssampling partitionthexyplaneintoagrid thecoordinatesofthecenterofeachgridbeingapairofelementsfromthecartesianproductz2 thesetofallorderedpairsofelements zi zj withziandzjbeingintegersfromz quantization fisafunctionthatassignsagray levelvalue arealnumberinr toeachdistinctpairofcoordinate x y imagesamplingandquantization representation bothspatialandgraylevelresolutionsdeterminethestoragesizeofanimage bytes e g spatialresolution 40 x40graylevelresolution 64 log264 6bits pixel thenumberofpixels 40 x40 1600pixels thestoragesize nocompression nooverhead 1600 x6 9600bits 1200bytes 1 17kb representation usually themandnarepositiveintegers andthenumberofgraylevelsisanintegerpowerof2 representation spatial intensitylevelresolution thespatialresolutionofanimageisdeterminedbyhowsamplingwascarriedout spatialresolutionsimplyreferstothesmallestdiscernabledetailinanimagevisionspecialistswilloftentalkaboutpixelsizegraphicdesignerswilltalkaboutdotsperinch dpi 5 1megapixels intensitylevelresolutionreferstothenumberofintensitylevelsusedtorepresenttheimagethemoreintensitylevelsused thefinerthelevelofdetaildiscernableinanimageintensitylevelresolutionisusuallygivenintermsofthenumberofbitsusedtostoreeachintensitylevel numberofbits numberofintensitylevels examples 1 2 0 1 2 4 00 01 10 11 4 16 0000 0101 1111 8 256 00110011 01010101 16 65 536 1010101010101010 spatial intensitylevelresolution spatial intensitylevelresolution 1024 1024 512 512 256 256 128 128 64 64 32 32 spatial intensitylevelresolution 128greylevels 7bpp 64greylevels 6bpp 32greylevels 5bpp 16greylevels 4bpp 8greylevels 3bpp 4greylevels 2bpp 2greylevels 1bpp 256greylevels 8bitsperpixel spatial intensitylevelresolution spatialresolution m ngraylevelresolution lhowmanysamplesandgraylevelsarerequiredforagoodapproximation resolution thedegreeofdiscernibledetail ofanimagedependsonsamplenumberandgraylevelnumber i e themoretheseparametersareincreased thecloserthedigitizedarrayapproximatestheoriginalimage but storage processingrequirementsincreaserapidlyasafunctionofn m andk spatial intensitylevelresolution thebigquestionwithresolutionisalways howmuchisenough thisalldependsonwhatisintheimageandwhatyouwouldliketodowithitkeyquestionsincludedoestheimagelookaestheticallypleasing canyouseewhatyouneedtoseewithintheimage spatial intensitylevelresolution thepictureontherightisfineforcountingthenumberofcars butnotforreadingthenumberplate spatial intensitylevelresolution zoomingoversamplingshrinkingsubsampling zoomingandshrinkingdigitalimages zooming thecreationofnewpixellocationstheassignmentofgraylevelstothosenewlocationsnearestneighborinterpolation nn pixelreplication aspecialcaseofnnnnproducescheckerboardeffect zoomingandshrinkingdigitalimages zooming bilinearinterpolationusingthefournnsofapoint g a g b g c g d arethegraylevelsofpinta b c d zoomingandshrinkingdigitalimages zoomingandshrinkingdigitalimages shrinking similarmannerasjustdescribedforzooming deleteexpandthegrid nearestneighborinterpolationbilinearinterpolation zoomingandshrinkingdigitalimages basicrelationshipsbetweenpixels apixelpat x y has2horizontaland2verticalneighbors x 1 y x 1 y x y 1 x y 1 thissetofpixelsiscalledthe4 neighborsofp n4 p the4diagonalneighborsofpare nd p x 1 y 1 x 1 y 1 x 1 y 1 x 1 y 1 n4 p nd p n8 p the8 neighborsofp definitions f x y digitalimagepixels q p basicrelationshipsbetweenpixels connectivity connectivitybetweenpixelsisimportant becauseitisusedinestablishingboundariesofobjectsandcomponentsofregionsinanimage twopixelsareconnectedif theyareneighbors i e adjacentinsomesense e g n4 p n8 p theirgraylevelssatisfyaspecifiedcriterionofsimilarity e g equality visthesetofgray levelvaluesusedtodefineadjacency e g v 1 foradjacencyofpixelsofvalue1 adjacency weconsiderthreetypesofadjacency 4 adjacency twopixelspandqwithvaluesfromvare4 adjacentifqisinthesetn4 p 8 adjacency p qare8 adjacentifqisinthesetn8 p m adjacency p qwithvaluesfromvarem adjacentifqisinn4 p orqisinnd p andthesetn4 p n4 q hasnopixelswithvaluesfromv adjacency mixedadjacencyisamodificationof8 adjacencyandisusedtoeliminatethemultiplepathconnectionsthatoftenarisewhen8 adjacencyisused v 1 8 adjacency m adjacency path 通路 asequenceofadjacentpixels forexample apathfrompixelpwithcoordinate x y topixelqwithcoordinate s t isdefined x0 y0 x1 y1 xn yn where x0 y0 x y xn yn s t xi yi and xi 1 yi 1 areadjacent 1 i n niscalledthelengthofthepath if x0 y0 xn yn thepathisaclosedpath 闭合通路 wecandefine4 8 andm pathdependingonthetypeofadjacency path path 4 path m path 8 path definitions letsrepresentasubsetofpixelsinanimage twopixelpandqaresaidtobeconnected 连通 insifthereexistsapathbetweenthem foranypixelpins thesetofpixelsthatareconnectedtoitinsiscalledaconnectedcomponent 连通分量 ofs ifitonlyhasoneconnectedcomponent thensetsiscalledaconnectedset 连通集 letrbeasubsetofpixelsinanimage wealsocalledraregion 区域 oftheimageisrisacon