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artificial intelligenceautomotive displaysautomotiveAutomatic colorizationabsorptionAmbient IlluminanceABYSS COLORARAugmented RealityappearanceArtificial IntelligenceAPPEARANCE MODEALBERS' PATTERNAMBIENT LIGHTAGINGAdaptive DisplayARCHAEOLOGYambient contrastAUDIOVISUALAge effectAdditive manufacturingambient lightingsAESTHETIC PERCEPTIONADAPTIVE TONE MAPPINGaugmented realityARIMAABSORBING MEDIUM
BORDER LUMINANCEBRDFBrightness compressionBrillianceBloodBAND PASSbrdf modellingbrightness perceptionBI-REFLECTANCE DISTRIBUTION FUNCTIONbrightnessbenchmarkingBRDF measurementsBANDINGBLUE BALANCEbackground luminanceBILIRUBINBinocular color
consistent colour appearanceCOMPUTATIONAL MODELINGColour-differencecolor capturecolor fidelitycolor inconstancycolor demosaickingcomputational modellingCOLOUR PREFERENCEcolor correctioncolor differenceCONTRASTCultural HeritageColor Appearance AttributesContrast sensitivityCMFCONSUMER IMAGESColorfulness in tone mappingCOLOR CENTERcolor image appearanceColor PerceptionCENTER SURROUNDcontrast sensitivity functionChromatic adaptationCOMPUTATIONAL PRINTINGColor directioncultural heritageCOLOUR APPEARANCE MODELColour correctionCorresponding Colourscolor volumeChromatic AdaptationCIELAB Color Spacecolor space conversionContrast sensitivity functionchromatic adaptationCONTOURINGCHROMATIC ADAPTATIONcolour characteristicsCAMERA PIPELINEcomputerized color vision testCOLOR FILTER ARRAY OPTIMIZATIONCOLOR SEMANTICScorrection modelColor Accuracycolor perceptionColor AppearanceCAM02-SCDcolor constancyColour appearance modelColour Appearance ModelCOLOURcolor visioncolor adaptation transformcamera spectral sensitivitiesColour Ordercamera spectral sensitivitycontrast visioncolor analysisCOLOR DIFFERENCEcolor renditioncolor matchingColor Correction Matrixcolor object recognitioncolor managementcolor pipelinecolor gamutcolor associationcolor appearanceCOLOR PALETTEScolor reproductionCorresponding colourcategorical colourcolour appearance modelCAMERA-RENDERED IMAGESCOLOR GAMUTColour matching functionsCOLORCIECAM16color apperancecolorimetrycomputationCOLORIMETRYcolour appearance ratingscultural differencescolor spacecolor appearance modelsCNN approachcameracolorcolour namingCOLOR VISION DEFICIENCIESCOLOR DISTANCECOLOR SPACE CONVERSIONcolor shiftColour Space OptimisationCHROMATICITYColour Deficiencycolor unmixingContrast EnhancementColor ImagingColor ScissioningCURVED OBJECTcaptureColorizationcomplexityCross-Media Colour Reproductioncolor representationcamera pipelinechromaticity gamutCAMERA SPECTRAL SENSITIVITYCONTRAST SENSITIVITYcomputational imagingColor filtersCOLOR CONSTANCY DATABASEcorresponding colorColor appearanceCorresponding ColorsColor correctionCIELABCOLOR PERCEPTIONcomputer visioncolour matching functionsCOLOR FILTERCOLOR CORRECTIONCOLOR AND LIGHT DESIGNCOLOR FILTER ARRAYcomputational color constancyCOLOR CONSTANCYCNNcolorfulnesschromatic contrast sensitivitycomputational photographyCONTRAST LIMITED HISTOGRAM EQUALIZATIONcontrast matchingcolor photographycross-modal associationCIECAM16 Colour Appearance ModelCOLOR SEPARATIONContrast enhancementCOLOR VISIONchromaCAMERAconstant hue locicardinalitycolored filtercontrast
digital cameraDEPTH CAMERAdielectric objectsdifferent colour backgrounddocument classificationDONALDSON MATRIXDIGITAL PRINTINGdiffuse reflectiondynamic range compressionDUPLEX HALFTONE PRINTSDIGITAL HALFTONINGDemosaicingDENTAL MATERIALdisplay calibrationdigital photographyDEPTH PERCEPTIONDUAL LIGHTING CONDITIONdye amount estimationdrug efficacyDIFFERENT COLOR CENTRESDIGITAL CAMERAdisplay technologydirect brightness matchingDICHROMATIC REFLECTIONDESCRIPTORdepthDehazingdeep neural networkdisplay metrologydegree of adaptationdataset generationDRIVING AUTOMATIONDE-RENDERINGDETECTIONDeep convolutional residual networkdigitizationDisplayDISPLAY GAMUTdeep learning
ERPedge preservationEuclidean Colour SpaceEFFECTIVE COLOR RENDERING AND TEMPERATUREERROR DIFFUSIONELDERLY USERSEMOJI
focal colorFOGRAfluorescence synthesisfavorabilityFLUORESCENT OBJECTSFILTER DESIGNFLUX TRANSFER MATRIXFACIAL COLOUR APPEARANCEFused deposition modelling (FDM)FACIAL ATTRACTIVENESSFarnsworth-Munsell 100 hue testFACIAL SKIN COLORfabric image preferenceFRACTALFOURIER SPECTRUMFALSE-COLOR COMPOSITESFOVEATED IMAGINGFilter DesignFACIAL SHAPEflicker photometryFAKE VS REAL IMAGEFWHM
goniometryGLOSSINESSGRAYgloss perceptiongeometric distortionsGEOMETRIC INTEGRATIONGraynessGhent AltarpieceGamut Mappinggloss measurementGAMUT VOLUME ETCGAMUT MAPPINGGREY-LEVEL CO-OCCURRENCE MATRIXglare in illusionsgeometric meangrayscale experimentGloss unevennessgamutGRAY BALANCEgloss
HANSHUMAN VISIONHDRHigh Dynamic Rangehuman visionHelmholtz-Kohlrausch effecthigh dynamic range imagingHISTOGRAM SPECIFICATIONHUE CIRCLEHIGH DYNAMIC RANGEHIGHLIGHT DETECTIONHUEHyperspectral ImagingHYPERSPECTRAL IMAGINGhelmholtz-kohlrauschHALFTONEHEAD-UP DISPLAYHUMAN COLOR PERCEPTIONHyperspectral reconstructionhapticsHigh-Dynamic-Rangehyperspectral imagingHUMAN COLOR VISIONhigh dynamic rangehue mixtureHALFTONE IMAGE REPRODUCTIONHunt Effecthighly saturated illuminanthighlighter mark featureshdrHigh dynamic rangeHERITAGEhalftoninghighlight detection
Ill-posed problemimage quality metricsINTER-REFLECTIONSimage qualityIMAGE QUALITYImage quality Assessmentilluminant estimationIMAGE CODINGILLUMINANCE LEVELSICCINK-USEimage reintegrationImage enhancementimage processingIMAGE SHARPENINGilluminant correctionImage Synthesisillumination estimationIATimage smoothingIMAGING CONDITION CORRECTIONinfraredImage EnhancementilluminationinterreflectionsINTER-OBSERVER DIFFERENCESImage editingintrinsic decompositionimagingimage quality datasetimage distortionImage Quality Evaluation MetricsIlluminant estimationIMAGE PROCESSINGIlluminationimage enhancementimage structureILLLUMINATION ESTIMATIONinterference photographyilluminant invarianceILLUMINANT ESTIMATIONINTERPOLATIONImage QualityINVERTIBILITYinformation compressionillumination invariance
JPEGjust noticeable difference
Kubelka-MunkKSM hue coordinatesKUBELKA-MUNK MODEL
LEGHLED-basedLightness/Brightness ScaleLEDslcdLipLuminancelightnessLost artLIGHT FIELDSLDR-to-HDR image mappingLCDLOGARITHMIC TONE MAPPINGLOW PASSLuminance MeterLIVER DISEASELippmannLIE GROUPSLIE ALGEBRASLOG POLAR TRANSFORMluminanceLOW-LEVEL VISIONlight field cameraLED LIGHTINGline elementLED light sourcesLOGISTICLEDlighting
MelaninModelingMEDIAMultispectral ImagesmetricsMatrix-RMCMLMULTIPLE LIGHT SOURCESmeasuresMetamericmaterial appearancememory colormicrofadingMRIMATERIAL-LIGHT INTERACTIONSMULTISPECTRAL IMAGINGMemory ColorsMagnitude estimationmultigrid optimizationmultichannel LED systemMixed RealityMEASURING GEOMETRYMULTI-SPECTRAL IMAGINGminimal assumptionMETAMERISMMEMORY COLORMDSmetallic surfacesMATERIAL PERCEPTIONmachine learningmultispectral imagingMaxwell methodMemory coloursmagnitude estimationmetamermultiple light sourcesmodelingMATERIAL APPEARANCEMAGNITUDE ESTIMATIONMultispectral ImagingMULTI-ILLUMINANTMETRICSMetrologymetallic objectssMultispectral imagingmixed reality
noncontact measurementneural networksnoise reduction.NEURAL NETWORKno-reference image quality modelno reference experimentNOISENEWTON'S ITERATIONnoiseNONLINEAR TRANSFORMATIONnoise modelNaturalnessNUMERICAL METHODS ON LIE GROUPSnon-linear-smoothingnumerical pathology
OPTIMIZATIONOmnidirectional Cameraonline psychophysicsoptical brightenersOPEN ENVIRONMENToptimizationObject RecognitionOBSERVER METAMERISMoledOptimal ColorsopticsObject Detectionobserver metamerism
pulse wavePsychophysicspatinasprint qualityPapanicolaou stainPhotometerpeak luminancePRIMARY COLOUR EDITINGPRINTPan-sharpeningPEAK LUMINANCEpill colorPROJECTORperceptibilitypulse ratepest controlprintingParametric effctparamerpersonal preferencesPSYCHOPHYSICSPigment classificationpolarization imagingprocessing fluencyPOLARIZED LIGHT CAMERAPRINTINGperceptionPERCEPTUAL COLOR GAMUTPerceptual QualityPlanckian illuminantpigment lightfastnesspsychophysical studyPATTERN ILLUMINATIONPseudocolorpolarizationphotometric stereoperceptual uniformityPIGMENTPerceptual Uniformityperceptual spacesPRINCIPAL COMPONENT ANALYSISprint reproduction differencepsychophysicsprefer skin colorperceptual experimentPERCEPTION
QUALITY ATTRIBUTESquadratic programmingqualityquality assuranceQUALITY ASSESSMENT
RAW SENSOR IMAGEREFLECTANCE ESTIMATIONred scaleregularized gradient kernelrandom CFAReal Scene ExperimentretinexRETINAreflectancered-green color vision deficiencyreflectance estimationROUND TRIPRECEPTIVE FIELDrepresentative colorrealnessremote tutorialsREFLECTION AND LUMINESCENCEreflective colour chartregressionradiometric correctionROOM BRIGHTNESSREMOTE DIAGNOSISrelightingRADIUS OF CURVATURE
standardizationSpace ApplicationsSPECTROPHOTOMETRYSPATIAL CHROMATIC CONTRAST SENSITIVITY FUNCTIONSparse CodingSubjective data collectionSPECTRAL RECONSTRUCTION FROM RGBSKIN COLORSKIN COLOUR PERCEPTIONSPECTRAL MEASUREMENTSPECTRAL REFLECTANCEspatialspecular reflectionSpectral imagingSpectral Reconstructionspectral imagingsurfaceSPECTRAL POWER DISTRIBUTIONsubstrate coloursStaircase method.saliencyscreened Poissonspectral renderingSeparationSMARTPHONEspectralSPATIO-SPECTRAL ANALYSISSpectral Reflectance Estimationsupport vector machineSPECTRAL TRANSMITTANCESPECTRAL RECONSTRUCTIONshapesubjective qualitySurface modificationscatteringscale-spacesurface preserving smoothingSPATIAL BRIGHTNESSSharpnesspectroscopySKIN COLOURSCATTERING MEDIUMSECONDARY ILLUMINATIONstress indexSIMULTANEOUS CONTRAST EFFECTSTRESSSnow imagingSmoothness ConstraintSPECTRAL ESTIMATIONSKIN COLOR MODELSPECTRAL REFLECTANCE AND TRANSMITTANCEspectral differenceSpatial Imagingsubjective evaluationSpectral Filter Arraysubsurface scatteringSKIN TONEskin segmentationSPECTRAL SIGNAL RECOVERYspectral reconstructionSUBJECTIVE EVALUATIONSTRUCTURAL COLORSpectral fusionskin colorsurface topographystereosopic displaySTATISTICAL SIGNIFICANCEspectral reflectanceSpectral similaritySPECTRAL SUPER-RESOLUTIONspectral reflectance estimationSPATIAL FREQUENCYSpatial frequency
TRAFFIC LIGHTtransitionTOTAL APPEARANCETRANSLUCENT MATERIALTEXTURE DESCRIPTORStexturetwo dimensional colour appearance scalesTONGUEtangibletemperaturethree-dimensional printingTMOTransparencyTONE MAPPINGTONE MAPPING OPERATORtone curvestextilestolerance ellipsoidTECHNICAL COMPARISONtexture characteristicsTone mappingtime coursetranslucencyTRISTIMULUS VALUEtime seriesTone Mappingtablet display
uniform colou spaceUNSHARP MASKINGUniform color spaceUNDERWATER PHOTOGRAPHYUNDERWATER IMAGE ENHANCEMENTUnmixing
visibility of gradientsvirtual productionvisual featuresVISUAL MODELVALIDATIONVisibility AppearanceVisual ComfortvisualizationVISUAL CLARITYvisual judgmentVisual appreciationVIRTUAL REALITYvisual computingvisual perceptionVISUAL COMFORTVisual AssessmentvisionVORA-VALUEVISUAL CORTEXVISUAL DATASETvisual comfortvividness
with reference experimentWCGWien's approximationWEIBULL DISTRIBUTIONWHITE-BALANCEworkflowWATERLIGHTWHITEPOINT ADAPTATIONwhitenesswraparound Gaussianwhite appearanceWHITE POINT
2.5D PRINTING2.5D printing2-d scale
3D MODEL3D printing3D-Anisotropic smoothing3D PRINTING3D shape analysis
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  16  0
Image
Page 1,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

In recent years, various methods have been developed for representing, encoding, and controlling colors in digital color-imaging systems. Although many of these methods have been based on the concept of “device-independent” color, none has proven to be completely successful for all systems and applications.This paper will describe a new paradigm for digital color encoding and color management. This single—and deceptively simple—“universal” color-management paradigm encompasses the functionality of all existing colorimaging systems. The paradigm, together with its unique color-encoding method, offers a complete solution to the difficult problem of supporting disparate types of input and output devices and media on a single system. Moreover it fulfills the most fundamental requirement of color management by providing unambiguous and unrestricted communication of color among systems of every kind.The paper will describe how this universal paradigm can be implemented in practice using color transformations consistent with specifications developed by the International Color Consortium (ICC), an industry group formed in 1993 to promote interoperability among color-managed systems. It also will be shown how a color managed system based on the universal paradigm can make optimum use of current interchange metrics, such as the KODAK Photo YCC Color Interchange Space used in the Photo CD System.

Digital Library: CIC
Published Online: January  1996
  11  0
Image
Pages 1 - 5,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

We propose a minimax technique to extract the optimum grid structure that will minimize the error in the interpolation of multidimensional functions using sequential linear interpolation (SLI). The error criterion we use is the maximum absolute error. We apply this method to the problem of color printer characterization.

Digital Library: CIC
Published Online: January  1996
  18  0
Image
Pages 5 - 9,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

This paper describes a new correction method for the color shift due to the illuminant changes based on the estimation of the spectral reflectance by a neural network. Proposed method has been compared to two conventional methods and evaluated. Our evaluation results show that the method can achieve better accuracy than other methods.

Digital Library: CIC
Published Online: January  1996
  9  1
Image
Pages 10 - 14,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

The introduction of ICC-based color management solutions promises a multitude of solutions to graphic arts imaging needs. To those of us who have been involving in the technology of graphic arts imaging, the best way to understand the performance of CMS is to test it. We decided to focus our initial effort on color matching aspects of the ICC profiles.To test the degree of color matching, a number of color patches that are reproduced by a hard copy output device in CIELAB values were specified as aim points. These colors were reproduced by the same output device according to the experimental design which involves three factors: ICC-compliant profiling tool, color rendering style, and work flow. The experimental design yields 8 sets of data. The degree of color matching is judged by average ΔE between the color produced and its original colorimetric specifications. We learned that the accuracy of color matching depends on the work flow, device profiling tools, and color rendering style. An average ΔE of 6.5 represents the best scenario in this particular color matching effort. Other factors such as precision or repeatability of the desktop printer and the measurement instrument which may have contributed differences in color matching were also discussed.

Digital Library: CIC
Published Online: January  1996
  14  0
Image
Pages 14 - 19,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

The construction of a system that uses CIE co-ordinate, and reflectance curve specified colour imaging as a colour communication tool is presented. Images are stored and manipulated as object hierarchies, with both an intrinsic object colour, and an object colour-set representing surface detail and texture.

Digital Library: CIC
Published Online: January  1996
  33  3
Image
Pages 19 - 22,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

Multispectral image capture (i.e, more than three channels) facilitates both more accurate tristimulus estimation and possibilities for spectral reconstruction of each scene pixel. A seven-channel camera was assembled using approximately 50 nm bandwidth interference filters, manufactured by Melles Griot, in conjunction with a Kodak Professional DCS 200m digital camera. Multichannel images were recorded for the Macbeth ColorChecker chart as an illustrative example. Three methods of spectral reconstruction were evaluated: spline interpolation, modified-discrete-sine-transformation (MDST) interpolation, and an approach based on principal-component analysis (PCA). The spectral reconstruction accuracy was quantified both spectrally and by computing CIELAB coordinates for a single illuminant and observer. The PCA-based technique resulted in the best estimated spectral-reflectance-factor functions. These results were compared with a least-squares colorimetric model that does not include the spectral-reconstruction step. This direct mapping resulted in similar colorimetric performance to the PCA method. The multispectral camera had marked improvement compared with traditional three-channel devices.

Digital Library: CIC
Published Online: January  1996
  20  0
Image
Pages 23 - 24,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

A description and analysis of analytical methods to between a digital camera device color space and device independent color spaces under varying lighting conditions will be presented. This approach has been evaluated in the production of an art paintings catalogue.

Digital Library: CIC
Published Online: January  1996
  8  0
Image
Volume 4
Issue 1

New quality measures for a set of color sensors—weighted quality factor qe, spectral characteristic restorability index qr and color reproducibility index Q—are proposed to practically evaluate color reproduction quality.Because these quantities take account of object color spectral characteristics, they are more reasonable and useful than previously-proposed quality measures. Simulation results clearly show a good relation between the proposed indices and color reproduction errors after a linear color correction.

Digital Library: CIC
Published Online: January  1996
  12  1
Image
Pages 28 - 31,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

Color errors in scanners arise from two sources: the non-colorimetric nature of the scanner sensitivities and the measurement noise. Several measures of goodness have been used to evaluate scanners based on these errors. In this paper, the trustworthiness of these measures is studied through simulations. A new measure incorporating both the above sources of errors and providing excellent agreement with perceived color error is also presented.

Digital Library: CIC
Published Online: January  1996
  13  0
Image
Pages 33 - 38,  © Society for Imaging Science and Technology 1996
Volume 4
Issue 1

The demand for accurate color reproduction has never been as high as it is today. Not only in the high-end electronic prepress market, but also in the desktop publishing and home office markets, the availability of both input and output devices is increasing rapidly.Most of the input devices today capture positive originals: scanners capture either reflective or transmissive originals; digital cameras are capable of capturing real life scenes as well.In some market segments (such as, e.g., the newspaper environment), there also is a definite interest in scanning negative originals. Especially with the new emerging APS standard for film (where manual manipulation of the film strips is no longer necessary), the demand for negative scanning will also increase in the home office market.Scanning negatives, however, is a very delicate process. Not only the input device should be characterised properly, but also the negative film itself is a parameter which needs to be studied carefully. On negative film, the information is stored inverted and due to the color dye layers within the negative film, there also is a density shift between the red, green and blue planes. The main problem, however, is caused by the fact that, due to the variations in the development process, the characteristics of a strip of developed negative film can differ considerably from other strips of the same film type.In this paper, we first give a brief survey of our approach to scanning negatives presented in the past. Then, we show how the unpredictable properties of negative films can cause this approach to fail and discuss some substantial improvements. In this respect, we show how the adaptive approach taken in the conventional photo-finishing environment can be used electronically. In a following section, we describe how the inverted positive image data can be transformed into a well-known, calibrated color space. In the last section, we briefly discuss the minimal requirements for an ideal negative scanner.

Digital Library: CIC
Published Online: January  1996

Keywords

[object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object]