ImageDev

- Getting Started
- ImageDev Python
- ImageDev C#
- ImageDev C++
- Licensing Information
- Redistributable Files
- Processing
- Image Manipulation
- - Synthetic Images
  - - RandomImage2d
    - RandomImage3d
    - RandomGaussianImage2d
    - RandomGaussianImage3d
    - RadialFunctionImage2d
    - RadialFunctionImage3d
    - OrientedLinesImage2d
    - OrientedLinesImage3d
    - RandomSphereImage3d
    - BoxGridImage2d
    - BoxGridImage3d
    - WedgeMaskImage3d
    - PointSpreadFunctionImage3d
  - Image Editing
  - - ConvertImage
    - ResetImage
    - CopyImage
    - PasteImage2d
    - PasteImage3d
    - CropImage2d
    - CropImage3d
    - SampleImage2d
    - SampleImage3d
    - InterlaceFrames2d
    - DeinterlaceFrames2d
  - Stack Operations
  - - SequenceConcatenation
    - SequenceToMultispectral
    - SequenceToVolume
    - VolumeToSequence
    - MultispectralToSequence
    - GetSliceFromVolume3d
    - SetSliceToVolume3d
    - GetObliqueSliceFromVolume3d
    - ImageStackProjection3d
    - VolumeProjection3d
- Geometry And Matching
- - Geometric Transforms
  - - Shearing3d
    - TranslateImage2d
    - TranslateImage3d
    - Flip2d
    - Flip3d
    - FlipImage2d
    - FlipImage3d
    - TransposeImage2d
    - TransposeImage3d
    - SwapQuadrants
    - Rotate2d
    - Rotate3d
    - RotationGenerator3d
    - Resize2d
    - Resize3d
    - RescaleByFactor
    - RescaleImageByFactor
    - ResampleAffine2d
    - ResampleAffine3d
    - ResampleElastic2d
    - SurfaceUnfolding3d
  - Pattern Recognition
  - - BinaryCorrelation2d
    - CrossCorrelation2d
    - CorrelationByDifference2d
    - CorrelationBySignChange2d
  - Registration
  - - ImagePrealignment3d
    - AffineRegistration
    - ElasticRegistration2d
- Arithmetic And Logic
- - Arithmetical Operations
  - - AbsoluteValue
    - AbsoluteMaximumWithImage
    - ArithmeticOperationWithImage
    - ArithmeticOperationWithValue
    - BlendWithImage
    - BlendWithValue
    - DirectionalBlending2d
    - CombineByMask
    - ImageFormula
    - EigenDecomposition2d
    - EigenDecomposition3d
  - Logical Operations
  - - MaskImage
    - LogicalOperationWithImage
    - LogicalOperationWithValue
    - LogicalNot
    - InvertImage
    - BitShift
- Image Filtering
- - Grayscale Transforms
  - - RescaleIntensity
    - StackRescaleIntensity
    - MatchContrast
    - HistogramEqualization
    - Clahe2d
    - Clahe3d
    - AdaptiveHistogramEqualization
    - ApplyGrayscaleLut
    - BackgroundImage2d
    - RadialBackgroundImage2d
    - ShadingCorrection
    - Exponential
    - Logarithm
    - Square
    - SquareRoot
    - RingArtifactRemoval3d
    - BeamHardeningCorrection3d
  - Color Transforms
  - - ColorToLightness
    - GetColorChannel
    - ColorToGrayscale
    - GrayscaleToColor
    - ColorSpaceConversion
    - ColorAntialiasing2d
    - BayerToRgb2d
    - DecorrelationStretch2d
    - BlendAlphaChannelWithImage
    - BlendAlphaChannelWithValue
  - Smoothing And Denoising
  - - BoxFilter2d
    - BoxFilter3d
    - MedianFilter2d
    - MedianFilter3d
    - GaussianFilter2d
    - GaussianFilter3d
    - ExponentialFilter2d
    - RecursiveExponentialFilter2d
    - RecursiveExponentialFilter3d
    - NonLocalMeansFilter2d
    - NonLocalMeansFilter3d
    - CurvatureDrivenDiffusion
    - BilateralFilter2d
    - BilateralFilter3d
    - MajorityFilter2d
    - MajorityFilter3d
    - SigmaFilter2d
    - SigmaFilter3d
    - Despeckle2d
    - Despeckle3d
    - NagaoFilter2d
    - NagaoFilter3d
    - SnnFilter2d
    - SnnFilter3d
    - FlowInpainting
  - Sharpening
  - - Deblurring2d
    - Delineate2d
    - Delineate3d
    - UnsharpMasking2d
    - UnsharpMasking3d
    - Shade2d
    - RichardsonLucyDeconvolution
  - Texture Filters
  - - MeanFilter2d
    - MeanFilter3d
    - VarianceFilter2d
    - VarianceFilter3d
    - SkewnessFilter2d
    - SkewnessFilter3d
    - KurtosisFilter2d
    - KurtosisFilter3d
    - ContrastFilter2d
    - ContrastFilter3d
    - VariationFilter2d
    - VariationFilter3d
    - EnergyFilter2d
    - EnergyFilter3d
    - EntropyFilter2d
    - EntropyFilter3d
    - AutocorrelationFilter
    - LocalAutocorrelationFilter2d
    - ImageCurvature2d
    - ImageCurvature3d
    - StructureEnhancementFilter2d
    - StructureEnhancementFilter3d
    - ScaleSpaceLogFilter2d
    - ScaleSpaceLogFilter3d
  - Frequency Domain
  - - ComplexFft
    - ComplexFftInverse
    - ComplexCenteredFft
    - ComplexCenteredFftInverse
    - CartesianToPolar2d
    - PolarToCartesian2d
    - ConvolutionWithImage
    - ConvolutionWithImage2d
    - RadialFrequencyFilter2d
    - GaborFiltering2d
- Edge Detection
- - Gradient
  - - GradientOperator2d
    - GradientOperator3d
    - VectorToMagnitude2d
    - VectorToMagnitude3d
    - GradientVector2d
    - GradientVector3d
    - GradientMagnitude2d
    - GradientMagnitude3d
    - MorphologicalGradient2d
    - MorphologicalGradient3d
    - RadialGradientCentered2d
    - RadialGradientCentered3d
    - RadialGradientContour2d
    - RadialGradientContour3d
    - RadialGradientLabel2d
    - RadialGradientLabel3d
  - Laplacian
  - - DobFilter2d
    - DobFilter3d
    - MorphologicalLaplacian
    - RecursiveLaplacian2d
    - RecursiveLaplacian3d
  - Other Derivatives
  - - GaussianDerivative2d
    - GaussianDerivative3d
    - GaussianHessianMatrix2d
    - GaussianHessianMatrix3d
    - GaussianGradientTensor2d
    - GaussianGradientTensor3d
  - Edge Marking
  - - ImageLocalMaxima2d
    - ImageLocalMaxima3d
    - RidgeDetection
    - ZeroCrossings2d
    - DijkstraShortestPath2d
    - EigenvaluesToStructureness2d
    - EigenvaluesToStructureness3d
    - TensorVoting2d
    - CannyEdgeDetector2d
    - CannyEdgeDetector3d
- Image Segmentation
- - Binarization
  - - Thresholding
    - ThresholdingByCriterion
    - AutoThresholdingBright
    - AutoThresholdingDark
    - ColorThresholding
    - TopHat
    - FeatureAdaptiveThresholding
    - AdaptiveThresholding2d
    - AdaptiveThresholding3d
    - HysteresisThresholding
  - Labeling
  - - Labeling2d
    - Labeling3d
    - ReorderLabels
    - AssignLabel
    - AddObjectToLabel
    - GroupCloseLabelsl2d
  - Feature Selection
  - - KillBorder2d
    - KillBorder3d
    - RemoveSmallSpots
    - ObjectBoundaries
    - BinarySmoothing2d
    - BinarySmoothing3d
    - LabelInterfaces
  - Separating And Filling
  - - FillHoles2d
    - FillHoles3d
    - RemoveSmallHoles
    - FillImageBorder3d
    - ExpandLabelBoundaries
    - SeparateObjects
    - HExtremaWatershed
    - SeparateObjectsByBlock
    - HExtremaWatershedByBlock
    - ConvexHull2d
  - Region Growing
  - - MarkerBasedWatershed2d
    - MarkerBasedWatershed3d
    - MarkerBasedWatershedByBlock
    - MarkerBasedWatershedBasinsByBlock
    - ExpandLabelsOnBackground
    - FloodFillThreshold2d
    - FloodFillThreshold3d
    - Waterpixel
  - Computational Geometry
  - - PolylineResampler2d
    - PolylineResampler3d
    - PolylineExtrusion3d
    - SurfaceFitting3d
- Mathematical Morphology
- - Erosion And Dilation
  - - Erosion2d
    - Erosion3d
    - Dilation2d
    - Dilation3d
    - ErosionDisk2d
    - ErosionDisk3d
    - ErosionBall3d
    - DilationDisk2d
    - DilationDisk3d
    - DilationBall3d
    - ErosionLine2d
    - ErosionLine3d
    - DilationLine2d
    - DilationLine3d
    - ErosionColor2d
    - DilationColor2d
    - SelectiveErosion2d
    - SelectiveErosion3d
    - SelectiveDilation2d
    - SelectiveDilation3d
  - Opening And Closing
  - - Opening2d
    - Opening3d
    - Closing2d
    - Closing3d
    - OpeningDisk2d
    - OpeningDisk3d
    - OpeningBall3d
    - ClosingDisk2d
    - ClosingDisk3d
    - ClosingBall3d
    - OpeningLine2d
    - OpeningLine3d
    - ClosingLine2d
    - ClosingLine3d
    - OpeningLineMaximum
    - ClosingLineMinimum
    - OpeningColor2d
    - ClosingColor2d
    - OpeningByReconstruction2d
    - OpeningByReconstruction3d
    - ClosingByReconstruction2d
    - ClosingByReconstruction3d
    - OpeningDiskByReconstruction2d
    - OpeningBallByReconstruction3d
    - ClosingDiskByReconstruction2d
    - ClosingBallByReconstruction3d
    - SelectiveOpening2d
    - SelectiveOpening3d
    - SelectiveClosing2d
    - SelectiveClosing3d
  - Hit Or Miss And Skeleton
  - - Skeleton
    - Centerline3d
    - InfluenceZones
    - Centroid2d
    - EndPoints2d
    - EndPoints3d
    - Pruning2d
    - Pruning3d
    - IsolatedPoints2d
    - IsolatedPoints3d
    - TriplePoints2d
    - InteriorPoints2d
    - ApplyMorphologicalLut2d
    - ApplyMorphologicalLut3d
  - Geodesic Transformations
  - - ReconstructionFromMarkers2d
    - ReconstructionFromMarkers3d
    - GrayscaleReconstruction2d
    - GrayscaleReconstruction3d
    - GrayscaleFillHoles2d
    - GrayscaleFillHoles3d
    - HExtrema2d
    - HExtrema3d
    - RegionalExtrema2d
    - RegionalExtrema3d
    - UltimateErosion
  - Distance Maps
  - - DistanceMap2d
    - DistanceMap3d
    - EuclideanDistanceMap3d
    - EuclideanDistanceMap2d
    - GeodesicDistanceMap
    - ClosestBoundaryPoints2d
    - ClosestBoundaryPoints3d
    - TimeMap
    - LocalThicknessMap3d
    - GeodesicPropagation2d
    - GeodesicMaxima
    - GeodesicMinima
- Image Analysis
- - Image Measurements
  - - BinaryAnalysis
    - Area2d
    - Area3d
    - IntensityIntegral2d
    - IntensityIntegral3d
    - ObjectCount
    - DistanceMean2d
    - EulerNumber2d
    - EulerNumber3d
    - FractalDimension
    - CurvatureIntegrals3d
  - Image Statistics
  - - IntensityExtrema
    - IntensityStatistics
    - IntensityMaskedStatistics
    - IntensityHistogram
    - IntensityBinHistogram
    - AutoThresholdingValue
    - CompareImage
    - CompareValue
    - SimilarityMetricValue
    - SsimValue2d
    - SsimValue3d
    - RegionSimilarity
    - RadialIntensityProfile2d
    - RadialIntensityProfile3d
    - CylindricalIntensityProfile3d
    - RadialAutocorrelationProfile
    - Cooccurrence2d
    - MeasureGaussianNoise
    - LocalMaxima2d
    - LocalMaxima3d
    - PeakExtractor
    - LocalDensityMap2d
    - LocalDensityMap3d
    - SegmentationMetrics
  - Individual Measurements
  - - VolumeFractionByLabel
    - LabelCountByRegion
    - VolumeFractionByRegion
    - LabelAnalysis
    - LabelFiltering
    - LabelFilteringAnalysis
    - FilterAnalysis
    - MeasurementToImage
    - FilterByMeasurement
    - SieveLabeling
  - Moments And Orientations
  - - MomentsOfInertia2d
    - MomentsOfInertia3d
    - InertiaMoment2d
    - Eccentricity2d
    - OrientationMapFourier3d
    - OrientationMatchingFourier3d
  - Morphometry
  - - ObjectVolume
    - TotalImageVolume
    - ObjectBoundaryArea
    - TotalImageBoundaryArea
    - AverageObjectAreaPerSlice3d
    - AverageObjectCountPerSlice3d
    - ObjectImageRatio
    - ObjectSpecificSurface
    - ObjectSurfaceDensity
    - PorosityPercentage3d
    - FragmentationIndex
    - StructureModelIndex3d
    - AverageObjectThickness3d
    - AverageSpaceThickness3d
    - ObjectLinearDensity3d
    - DegreeOfAnisotropy
  - Propagation
  - - AxisPropagationBinary3d
    - AxisPropagationBinaryWithSeeds3d
    - AxisPropagationLabel3d
    - AxisPropagationLabelWithSeeds3d
    - AxisPropagationDistance3d
    - EstimateShortestPath3d
    - PercolationThreshold3d
    - AxisConnectivity3d
    - CentroidPathTortuosity3d
- Machine Learning
- - Classification
  - - AutoSegmentation3Phases
    - AutoIntensityClassification
    - SupervisedTextureClassification2d
    - SupervisedTextureClassification3d
    - TextureClassificationCreate
    - TextureClassificationTrain
    - TextureClassificationApply
  - Deep learning
  - - OnnxPredictionFiltering2d
    - OnnxPredictionFiltering3d
    - OnnxPredictionSegmentation2d
    - OnnxPredictionSegmentation3d
    - OnnxPredictionTwoOutputs2d
    - OnnxPredictionTwoOutputs3d
- Business Application
- - Dental
  - - DentalWallSegmentation3d
    - DentalMeshExtraction3d
    - DentalUnfolding3d
- IO
- - Image Data
  - - ReadVipImage
    - WriteVipImage
  - Algebraic Data
  - - ReadArrayXd
    - ReadArrayXi64
    - WriteArrayXd
    - WriteArrayXi64
  - Model
  - - ReadOnnxModel
- CUDA
- - Geometry And Matching
  - - CudaCrossCorrelation2d
    - CudaTemplateMatching3d
  - Image Filtering
  - - CudaBoxFilter2d
    - CudaMedianFilter2d
    - CudaGaussianFilter3d
    - CudaAdaptiveManifoldNlmFilter
  - Mathematical Morphology
  - - CudaErosion2d
    - CudaDilation2d
    - CudaOpening2d
    - CudaClosing2d
- Utilities
- Initialization
- Processing
- Syntax Check
- Visualization
- Data Object
- Image Data
- Kernels
- - MorphologicalKernel2d
  - MorphologicalKernel3d
  - MorphologicalLut2d
  - MorphologicalLut3d
- Model
- - TextureClassificationModel
  - OnnxModel
- Global Measurements
- - Image Measurements
  - - Area2dMsr
    - Area3dMsr
    - IntensityIntegral2dMsr
    - IntensityIntegral3dMsr
    - ObjectCountMsr
    - DistanceMeanMsr
    - Euler2dMsr
    - Euler3dMsr
    - FractalMsr
    - CurvatureIntegralsMsr
  - Image Statistics
  - - IntensityExtremaMsr
    - StatisticsMsr
    - IntensityHistogramMsr
    - IntensityBinHistogramMsr
    - CompareMsr
    - SimilarityMetricValueMsr
    - SsimValueMsr
    - RegionSimilarityMsr
    - SegmentationMetricsObjectMsr
    - SegmentationMetricsImageMsr
    - CylindricalIntensityProfileMsr
    - RadialIntensityProfileMsr
    - CorrelationMsr
    - CoocurrrenceMsr
    - GaussianNoiseMsr
    - LocalMaximaMsr
    - PeakExtractorMsr
  - Individual Measurements
  - - VolumeFractionByLabelMsr
    - LabelCountByRegionMsr
    - VolumeFractionByRegionMsr
  - Moments And Orientations
  - - MomentsOfInertia2dMsr
    - MomentsOfInertia3dMsr
    - MeanInertiaMomentMsr
    - Eccentricity2dMsr
    - LocalOrientation3dMsr
  - Morphometry
  - - ObjectVolumeMsr
    - ImageVolumeMsr
    - ObjectBoundaryAreaMsr
    - ImageBoundaryAreaMsr
    - AverageObjectAreaPerSliceMsr
    - AverageObjectCountPerSliceMsr
    - ObjectImageRatioMsr
    - ObjectSpecificSurfaceMsr
    - ObjectSurfaceDensityMsr
    - PorosityPercentageMsr
    - FragmentationMsr
    - StructureModelIndexMsr
    - AverageObjectThicknessMsr
    - AverageSpaceThicknessMsr
    - ObjectLinearDensityMsr
    - AnisotropyDegreeMsr
  - Propagation
  - - AxisPropagationBinary3dMsr
    - AxisPropagationLabel3dMsr
    - AxisPropagationDistance3dMsr
    - PercolationThreshold3dMsr
    - CentroidTortuosity3dMsr
  - Geometry And Matching
  - - CrossCorrelation2dMsr
  - Edge Detection
  - - CannyEdgeDetectorMsr
  - Image Segmentation
  - - AutoThresholdingMsr
    - AutoSegmentation3PhasesMsr
  - Mathematical Morphology
  - - ApplyMorphologicalLutMsr
- Analysis Measurements
- - AnalysisMsr
  - Measurement
  - Native Measurements
  - - Geometry
    - Feret
    - Intensity
    - Histogram
    - Inertia
    - Locate
    - Segment
    - Cooccurrence
  - CustomMeasurement
  - MeasurementAttributes
  - - Quantile
    - Histogram
    - Feret2d
    - Feret3d
    - NeighborCount
    - Cooccurrence
    - Breadth3d
  - MeasurementGroup
- Measurement Information
- - FieldInformation
  - CustomMeasurementInfo
- CUDA
- - CudaContext
- User Guide
- First Steps
- - Hello ImageDev
  - Initialize ImageDev
- Image Management
- - Create 2D Image
  - Create 3D Image
  - Data Transfer
- Standard Processing
- - Denoising
  - Thresholding
- Image Analysis
- - Global Analysis
  - Label Analysis
  - Measurement Browsing
  - Measurement Group
  - Custom Analysis
- Advanced Processing
- - Algorithm Tracking
- Typical Use Cases
- - Particle Analysis
  - Pore Analysis

# CrossCorrelation2d

Performs a correlation by convolution between a two-dimensional grayscale image and a grayscale kernel.
Access to parameter description
For an introduction: section Image Correlation.
This algorithm performs a cross-correlation, or correlation with a mutiplication metric, between a gray level image $I$ and a gray level kernel $K$, returning the correlation image $O$.
The different possibilities for selecting a correlation peak are presented below using a 1-D correlation between an image and kernel. In the image, the kernel appears 6 times with different contrast and luminosity.
Figure 1. 1D image and kernel
The 6 examples show the kernel appearing with different contrast and luminosity.
Figure 2. Examples of possible 1D correlation
The cross-correlation metric is computed in accordance with the CorrelationMode parameter.

### DIRECT Correlation Mode

$$ O(n,m)=\sum_{i=1}^{kx} \sum_{j=1}^{ky} K(i,j)\times I\left(n+i-\frac{kx}{2},m+j-\frac{ky}{2}\right) $$ In this mode, 3 of the 6 patterns matching the kernel are detected. These are only high luminosity patterns. The best matching is obtained for the highest contrast and luminosity pattern.
Figure 3. Example of 1D direct cross-correlation

### MEAN Correlation Mode

$$ O(n,m)=\sum_{i=1}^{kx} \sum_{j=1}^{ky} \left(K(i,j)-\mu(K)\right)\times \left(I(n+i-\frac{kx}{2},m+j-\frac{ky}{2})-\mu(I)(n,m)\right) $$ In this mode, 2 of the 6 patterns matching the kernel are detected. These are only high contrast patterns. The confidence rate is the same for the two 2 patterns.
Figure 4. Example of 1D mean cross-correlation

### VARIANCE Correlation Mode

$$ O(n,m)=\frac{\sum\limits_{i=1}^{kx} \sum\limits_{j=1}^{ky} K(i,j)\times I(n+i-\frac{kx}{2},m+j- \frac{ky}{2})}{\sqrt{\sigma^2(K)\times \sigma^2(I)(n,m)}} $$ In this mode, 2 patterns matching the kernel are detected, with the same confidence rate.
Figure 5. Example of 1D variance cross-correlation

### MEAN_VARIANCE Correlation Mode

$$ O(n,m)=\frac{\sum\limits_{i=1}^{kx} \sum\limits_{j=1}^{ky} \left(K(i,j)-\mu(K)\right)\times \left(I(n+i-\frac{kx}{2},m+j-\frac{ky}{2})-\mu(I)(n,m)\right)}{\sqrt{\sigma^2(K)\times \sigma^2(I)(n,m)}} $$ In this mode, the 6 patterns matching the kernel are detected, with the same confidence rate.
Figure 6: Example of 1D mean and variance cross-correlation
where: $$ \mu(K)=\frac{\sum\limits_{i=1}^{kx} \sum\limits_{j=1}^{ky} K(i,j)}{kx\times ky} $$ $$ \mu(I)(n,m)=\frac{\sum\limits_{i=1}^{kx} \sum\limits_{j=1}^{ky} I(n+i,m+j)}{kx\times ky} $$ $$ \sigma^2(K)=\sum_{i=1}^{kx} \sum_{j=1}^{ky} \left(K(i,j)-\mu(K)\right)^{2} $$ $$ \sigma^2(I)(n,m)=\sum_{i=1}^{kx} \sum_{j=1}^{ky} \left( I(n+i-\frac{kx}{2},m+j-\frac{ky}{2} ) - \mu(I)(n,m)\right)^{2} $$
Note: This algorithm returns the main correlation peak in the outputMeasurement object. More correlation peaks can be extracted from the outputImage correlation image with the LocalMaxima2d algorithm
See also

- CudaCrossCorrelation2d
- BinaryCorrelation2d
- CorrelationByDifference2d
- CorrelationBySignChange2d
- LocalMaxima2d

##
Function Syntax

This function returns a CrossCorrelation2dOutput structure containing outputImage and outputMeasurement.

    // Output structure of the crossCorrelation2d function.
    struct CrossCorrelation2dOutput
    {
        /// The output correlation image. Its dimensions are forced to the same values as the input. Its data type is forced to floating point.
        std::shared_ptr< iolink::ImageView > outputImage;
        /// The correlation matching results.
        CrossCorrelation2dMsr::Ptr outputMeasurement;
    };

    // Function prototype
    CrossCorrelation2dOutput
    crossCorrelation2d( std::shared_ptr< iolink::ImageView > inputImage,
                        std::shared_ptr< iolink::ImageView > inputKernelImage,
                        CrossCorrelation2d::CorrelationMode correlationMode,
                        std::shared_ptr< iolink::ImageView > outputImage = nullptr,
                        CrossCorrelation2dMsr::Ptr outputMeasurement = nullptr );

This function returns a tuple containing output_image and output_measurement.

    // Function prototype.
    cross_correlation_2d(input_image: idt.ImageType,
                         input_kernel_image: idt.ImageType,
                         correlation_mode: CrossCorrelation2d.CorrelationMode = CrossCorrelation2d.CorrelationMode.DIRECT,
                         output_image: idt.ImageType = None,
                         output_measurement: Union[Any, None] = None) -> Tuple[idt.ImageType, CrossCorrelation2dMsr]

This function returns a CrossCorrelation2dOutput structure containing outputImage and outputMeasurement.

    /// Output structure of the CrossCorrelation2d function.
    public struct CrossCorrelation2dOutput
    {
        ///
        /// The output correlation image. Its dimensions are forced to the same values as the input. Its data type is forced to floating point.
        ///
        public IOLink.ImageView outputImage;
        /// The correlation matching results.
        public CrossCorrelation2dMsr outputMeasurement;
    };

    // Function prototype.
    public static CrossCorrelation2dOutput
    CrossCorrelation2d( IOLink.ImageView inputImage,
                        IOLink.ImageView inputKernelImage,
                        CrossCorrelation2d.CorrelationMode correlationMode = ImageDev.CrossCorrelation2d.CorrelationMode.DIRECT,
                        IOLink.ImageView outputImage = null,
                        CrossCorrelation2dMsr outputMeasurement = null );

##
Class Syntax

    // Command constructor.
    CrossCorrelation2d();

    /// Gets the inputImage parameter.
    /// The input grayscale image.
    std::shared_ptr< iolink::ImageView > inputImage() const;
    /// Sets the inputImage parameter.
    /// The input grayscale image.
    void setInputImage( std::shared_ptr< iolink::ImageView > inputImage );

    /// Gets the inputKernelImage parameter.
    /// The correlation kernel.
    std::shared_ptr< iolink::ImageView > inputKernelImage() const;
    /// Sets the inputKernelImage parameter.
    /// The correlation kernel.
    void setInputKernelImage( std::shared_ptr< iolink::ImageView > inputKernelImage );

    /// Gets the correlationMode parameter.
    /// The normalization mode for correlation.
    CrossCorrelation2d::CorrelationMode correlationMode() const;
    /// Sets the correlationMode parameter.
    /// The normalization mode for correlation.
    void setCorrelationMode( const CrossCorrelation2d::CorrelationMode& correlationMode );

    /// Gets the outputImage parameter.
    /// The output correlation image. Its dimensions are forced to the same values as the input. Its data type is forced to floating point.
    std::shared_ptr< iolink::ImageView > outputImage() const;
    /// Sets the outputImage parameter.
    /// The output correlation image. Its dimensions are forced to the same values as the input. Its data type is forced to floating point.
    void setOutputImage( std::shared_ptr< iolink::ImageView > outputImage );

    /// Gets the outputMeasurement parameter.
    /// The correlation matching results.
    CrossCorrelation2dMsr::Ptr outputMeasurement() const;

    // Method to launch the command.
    void execute();

    # Property of the inputImage parameter.
    CrossCorrelation2d.input_image
    # Property of the inputKernelImage parameter.
    CrossCorrelation2d.input_kernel_image
    # Property of the correlationMode parameter.
    CrossCorrelation2d.correlation_mode
    # Property of the outputImage parameter.
    CrossCorrelation2d.output_image
    # Property of the outputMeasurement parameter.
    CrossCorrelation2d.output_measurement

    // Method to launch the command.
    execute()

    // Command constructor.
    CrossCorrelation2d()

    // Property of the inputImage parameter.
    CrossCorrelation2d.inputImage
    // Property of the inputKernelImage parameter.
    CrossCorrelation2d.inputKernelImage
    // Property of the correlationMode parameter.
    CrossCorrelation2d.correlationMode
    // Property of the outputImage parameter.
    CrossCorrelation2d.outputImage
    // Property of the outputMeasurement parameter.
    CrossCorrelation2d.outputMeasurement

    // Method to launch the command.
    Execute()

## Parameters

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| inputImage | The input grayscale image. | Image | Grayscale | nullptr
| inputKernelImage | The correlation kernel. | Image | Grayscale | nullptr
| correlationMode | The normalization mode for correlation.
DIRECT | Direct correlation (no normalization).
MEAN | Mean normalized correlation (luminosity).
VARIANCE | Variance normalized correlation (contrast).
MEAN_VARIANCE | Mean and variance normalized correlation (luminosity and contrast).
| Enumeration |  | DIRECT
| outputImage | The output correlation image. Its dimensions are forced to the same values as the input. Its data type is forced to floating point. | Image |  | nullptr
| outputMeasurement | The correlation matching results. | CrossCorrelation2dMsr |  | nullptr

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| input_image | The input grayscale image. | image | Grayscale | None
| input_kernel_image | The correlation kernel. | image | Grayscale | None
| correlation_mode | The normalization mode for correlation.
DIRECT | Direct correlation (no normalization).
MEAN | Mean normalized correlation (luminosity).
VARIANCE | Variance normalized correlation (contrast).
MEAN_VARIANCE | Mean and variance normalized correlation (luminosity and contrast).
| enumeration |  | DIRECT
| output_image | The output correlation image. Its dimensions are forced to the same values as the input. Its data type is forced to floating point. | image |  | None
| output_measurement | The correlation matching results. | CrossCorrelation2dMsr |  | None

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| inputImage | The input grayscale image. | Image | Grayscale | null
| inputKernelImage | The correlation kernel. | Image | Grayscale | null
| correlationMode | The normalization mode for correlation.
DIRECT | Direct correlation (no normalization).
MEAN | Mean normalized correlation (luminosity).
VARIANCE | Variance normalized correlation (contrast).
MEAN_VARIANCE | Mean and variance normalized correlation (luminosity and contrast).
| Enumeration |  | DIRECT
| outputImage | The output correlation image. Its dimensions are forced to the same values as the input. Its data type is forced to floating point. | Image |  | null
| outputMeasurement | The correlation matching results. | CrossCorrelation2dMsr |  | null

## Object Examples

    auto polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

    CrossCorrelation2d crossCorrelation2dAlgo;
    crossCorrelation2dAlgo.setInputImage( polystyrene );
    crossCorrelation2dAlgo.setInputKernelImage( polystyrene );
    crossCorrelation2dAlgo.setCorrelationMode( CrossCorrelation2d::CorrelationMode::DIRECT );
    crossCorrelation2dAlgo.execute();

    std::cout << "outputImage:" << crossCorrelation2dAlgo.outputImage()->toString();
    std::cout << "matchingPositionX: " << crossCorrelation2dAlgo.outputMeasurement()->matchingPositionX( 0 ) ;

    polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))

    cross_correlation_2d_algo = imagedev.CrossCorrelation2d()
    cross_correlation_2d_algo.input_image = polystyrene
    cross_correlation_2d_algo.input_kernel_image = polystyrene
    cross_correlation_2d_algo.correlation_mode = imagedev.CrossCorrelation2d.DIRECT
    cross_correlation_2d_algo.execute()

    print("output_image:", str(cross_correlation_2d_algo.output_image))
    print("matchingPositionX: ", str(cross_correlation_2d_algo.output_measurement.matching_position_x(0)))

    ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );

    CrossCorrelation2d crossCorrelation2dAlgo = new CrossCorrelation2d
    {
        inputImage = polystyrene,
        inputKernelImage = polystyrene,
        correlationMode = CrossCorrelation2d.CorrelationMode.DIRECT
    };
    crossCorrelation2dAlgo.Execute();

    Console.WriteLine( "outputImage:" + crossCorrelation2dAlgo.outputImage.ToString() );
    Console.WriteLine( "matchingPositionX: " + crossCorrelation2dAlgo.outputMeasurement.matchingPositionX( 0 ) );

## Function Examples

    auto polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

    auto result = crossCorrelation2d( polystyrene, polystyrene, CrossCorrelation2d::CorrelationMode::DIRECT );

    std::cout << "outputImage:" << result.outputImage->toString();
    std::cout << "matchingPositionX: " << result.outputMeasurement->matchingPositionX( 0 ) ;

    polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))

    result_output_image, result_output_measurement = imagedev.cross_correlation_2d(polystyrene, polystyrene, imagedev.CrossCorrelation2d.DIRECT)

    print("output_image:", str(result_output_image))
    print("matchingPositionX: ", str(result_output_measurement.matching_position_x(0)))

    ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );

    Processing.CrossCorrelation2dOutput result = Processing.CrossCorrelation2d( polystyrene, polystyrene, CrossCorrelation2d.CorrelationMode.DIRECT );

    Console.WriteLine( "outputImage:" + result.outputImage.ToString() );
    Console.WriteLine(  "matchingPositionX: " + result.outputMeasurement.matchingPositionX( 0 )  );

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