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russ john c.; neal f. brent - the image processing handbook

The Image Processing Handbook

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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

CRC Press

Pubblicazione: 08/2017
Edizione: Edizione nuova, 7° edizione





Note Editore

Consistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more. Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition: Features a greater range of computationally intensive algorithms than previous versions Provides better organization, more quantitative results, and new material on recent developments Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines Presents 500+ entirely new figures and images, with more than two-thirds appearing in color The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.




Sommario

IntroductionAbout this textA word of cautionA personal note Acquiring ImagesHuman reliance on imagesExtracting informationVideo camerasCCD camerasCMOS detectorsCamera artifacts and limitationsColor camerasCamera resolutionElectronics and bandwidth limitationsHandling color dataColor encodingOther image sourcesPixelsTonal resolutionThe image contentsCamera limitationsNoiseHigh-depth imagesFocusingColor displaysImage typesMultiple imagesImaging requirements Printing and StorageHard copiesHalftoningDots on paperColor printingAdding black—CMYKPrinting hardwareFilm recordersPresentation toolsFile storageStorage mediaMagnetic recordingDatabases for imagesSearching by contentBrowsing and thumbnailsFile formatsLossless codingReduced color palettesJPEG compressionWavelet compressionFractal compressionDigital movies Human VisionWhat we see and whyRecognitionTechnical specsSeeing colorAcuityWhat the eye tells the brainSpatial comparisonsLocal to global hierarchiesGroupingIt’s about timeThe third dimensionHow versus whatSeeing what isn’t there, and vice versaImage compressionA world of lightSize mattersShape (whatever that means)ContextArrangements must be madeSeeing is believingLearning more Correcting Imaging DefectsColor adjustmentsHue, saturation, intensityOther spacesColor correctionNoisy imagesNeighborhood averagingGaussian smoothingNeighborhood rankingThe color medianMore median filtersWeighted, conditional, and adaptive neighborhoodsOther neighborhood noise reduction methodsDefect removal, maximum entropy, and maximum likelihoodNonuniform illuminationFitting a background functionRank levelingColor imagesNonplanar viewsComputer graphicsGeometric distortionAlignmentInterpolationMorphing Image Enhancement in the Spatial DomainPurposes for enhancementContrast expansionFalse color lookup tables (LUTs) Contrast manipulationHistogram equalizationContrast in color imagesLocal equalizationLaplacian sharpeningThe unsharp maskDerivativesEdges and gradientsEdge orientationMore edge detectorsRank-based methodsTextureImplementation notesImage mathSubtracting imagesMultiplication and divisionPrincipal component analysisPrincipal component analysis for contrast enhancementOther image combinationsCross-correlation Processing Images in Frequency SpaceAbout frequency spaceThe Fourier transformFourier transforms of simple functionsMoving to two dimensionsFrequencies and spacingsPreferred orientationTexture and fractalsRemoving selected frequenciesPeriodic noise removalSelection of periodic informationConvolutionDeconvolutionNoise and Wiener deconvolutionOther deconvolution methodsAdditional notes on deconvolutionTemplate matching and correlationAutocorrelationWavelets Segmentation and ThresholdingBrightness thresholdingAutomatic settingsMultiband imagesColor thresholdingThresholding from textureMultiple thresholding criteriaTextural orientationRegion boundariesNoise and overlapsSelecting smooth boundariesConditional histogramsBoundary linesContoursCluster analysisMore segmentation methodsImage representation Processing Binary ImagesBoolean operationsCombining Boolean operationsMasksFrom pixels to featuresFilling holesMeasurement gridsBoolean logic with featuresSelecting features by locationDouble thresholdingErosion and dilationOpening and closingIsotropyMeasurements using erosion and dilationExtension to grayscale imagesNeighborhood parametersExamples of useEuclidean distance mapWatershed segmentationUltimate eroded pointsSkeletonsTopologyBoundary linesCombining skeleton and Euclidean distance map Image MeasurementsPhotogrammetryComparisonsGlobal measurementsVolumeSurface areaGrain sizeMultiple surfacesLengthThicknessSampling strategiesDetermining numberCurvature, connectivity, and the DisectorAnisotropy and gradientsSize distributionClassical stereology (unfolding) Feature MeasurementsBrightness measurementsDensityBrightness profilesColor valuesDetermining locationOrientationNeighbor relationshipsSeparation distanceAlignmentThe linear Hough transformThe circular Hough transformCountingSpecial counting proceduresFeature sizeCircles and ellipsesCaliper dimensionsPerimeter Characterizing ShapeDescribing shapeDimensionless ratiosEffects of orientation"Like a circle"An example: LeavesTopology and the skeletonBoundariesShock graphsFractal dimensionMeasurement techniquesHarmonic analysisChain codeAn example: Arrow pointsWaveletsMomentsAn example: DandelionZernike momentsLandmarks Correlation, Classification, Identification, and MatchingA variety of purposesMatchingCross-correlationCurvature scale spaceClassificationDistributions and decision pointsLinear discriminant analysis (LDA) and principal component analysis (PCA) Class definitionUnsupervised learningAre groups different? Neural netsk-Nearest neighborsParametric descriptionBayesian statisticsA comparisonHarmonic analysis and invariant momentsSpecies examplesCorrelationLandmark data 3D ImagingMore than two dimensionsVolume imaging versus sectionsSerial sectionsRemoving layersReconstructionConfocal microscopyStereo viewingTomographyTomographic reconstructionReconstruction artifactsAlgebraic reconstructionMaximum entropyImaging geometriesOther signalsBeam hardening and other issues3D tomographyDual energy methodsMicrotomography3D reconstruction and visualizationSlices and surfacesMarching cubesVolumetric displaysRay tracing 3D Processing and MeasurementProcessing voxel arraysWhen the z-axis is differentMultiple image setsThresholding and segmentationMorphological operations and structural measurementsSkeletonsSurface and volumeQuantitative use of reconstructionsMethods for object measurementsSizeExamples of object measurementsOther object measurementsLimitationsIndustrial applicationsComparison to stereological measurementsSpherical harmonics, wavelets, and fractal dimensionOther applications and future possibilities Imaging SurfacesProducing surfacesImaging by physical contactNoncontacting measurementsShape from shading and polynomial texture mapMicroscopy of surfacesStereoscopyMatching pointsComposition imagingProcessing of range imagesProcessing of composition mapsData presentation and visualizationSurface renderingMeasurementsProfilesRepresenting elevation dataThe surface measurement suiteHybrid propertiesTopographic analysisFractal dimensions References




Autore

John C. Russ has used image processing and analysis as a principal tool for understanding and characterizing the structure and function of materials throughout his more than 50-year career as a scientist and educator. Much of Russ' research work has been concerned with the microstructure and surface topography of metals and ceramics. He has received funding for his research from government agencies and from industry. Teaching the principles and methods involved to several thousand students—in addition to consulting for many industrial clients—has further broadened Dr. Russ’ experience and the scope of applications for image processing and analysis. He continues to write and consult for a variety of companies (and to provide expert testimony in criminal and civil cases). He also still teaches image processing and analysis workshops worldwide and reviews publications and funding proposals. F. Brent Neal is a scientist and industrial researcher with Milliken Research Corporation, where he currently leads the central materials characterization and analytical chemistry facility. In this role, he leads efforts in technology and product development through deep understanding of materials performance. He has three patents issued or pending based on his work in polymer-matrix composites. Prior to his tenure at Milliken Research Corporation, he consulted and developed bespoke software for quantitative image analysis. He received his Ph.D in solid-state physics from Louisiana State University in 2002. Over the course of his career, he has measured and analyzed images from many different fields and his experience in materials characterization and measurement has been applied everywhere from the lab bench to manufacturing plants.










Altre Informazioni

ISBN:

9781138747494

Condizione: Nuovo
Dimensioni: 10 x 7 in Ø 4.67 lb
Formato: Brossura
Pagine Arabe: 1035


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