Wolfram Language & System 11.0 (2016)|Legacy Documentation

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returns a distance measure between image1 and image2.

places the center of image2 at position pos in image1.

places the point pos2 of image2 at position pos1 in image1.

Details and OptionsDetails and Options

  • ImageDistance[image1,image2] centers image2 in image1 and returns the distance between the overlapping regions in the two images.
  • ImageDistance works with arbitrary 2D and 3D images.
  • Images should either have the same number of channels or one should be a single-channel image. If either of image1 or image2 is a single-channel image, the channel is replicated to match the number of channels in the other image.
  • Position specification pos can be of the form:
  • {x,y} or {x,y,z}absolute pixel position
    Scaled[{sx,}]scaled position from 0 to 1 across the object
    Centercenter alignment
    Left,Right axis in both 2D and 3D
    Bottom,Top axis in 2D, axis in 3D
    Front,Back axis in 3D
    {posx,}a list of named positions
  • If alignment along each axis is not given, it is assumed to be Center.
  • The following options can be given:
  • DistanceFunctionEuclideanDistancedistance function to use
    MaskingAllregion of interest
  • Some typical distance function settings include:
  • EuclideanDistanceEuclidean distance (default)
    SquaredEuclideanDistancesquared Euclidean distance
    NormalizedSquaredEuclideanDistancenormalized squared Euclidean distance
    ManhattanDistanceManhattan or "city block" distance
    CosineDistanceangular cosine distance
    CorrelationDistancecorrelation coefficient distance
    ffunction f that is given the overlapping regions of the two images as arguments
  • The following special distance functions are also supported:
  • "MeanEuclideanDistance"mean Euclidean distance
    "MeanSquaredEuclideanDistance"mean squared Euclidean distance
    "RootMeanSquare"mean squared root distance
    {"MeanReciprocalSquaredEuclideanDistance",λ}one minus the mean of the robust distances , where is the Euclidean distance of corresponding pixels (default )
    {"MutualInformationVariation",n}joint entropy minus mutual information using n-bin histogram (default )
    {"NormalizedMutualInformationVariation",n}the mutual information variation divided by the joint entropy using n-bin histogram (default )
    {"DifferenceNormalizedEntropy",n}entropy of the difference image using n-bin histogram (default )
    "MeanPatternIntensity"mean local pattern intensity difference
    "GradientCorrelation"mean of the correlation distances between the spatial derivatives
    "MeanReciprocalGradientDistance"one minus the mean of the distances , with values and variances of the spatial derivatives along dimension s of imagei
    {"EarthMoverDistance",n}earth mover distance using n-bin histogram
  • Using Masking->roi, a region of interest in image1 is specified. With Masking->{roi1,roi2}, the intersection of roi1 and roi2 on the overlapped images is used.
  • Predefined ImageDistance metrics are symmetric and non-negative. However, some distances may not satisfy the triangle inequality. The distance between two images can be 0 with some methods, even if they are not identical. User-defined functions might break these properties.
  • If there are no overlapping regions or the measure cannot be determined, Indeterminate is returned. »
Introduced in 2012
| Updated in 2016