Wolfram Language & System 10.3 (2015)|Legacy Documentation

This is documentation for an earlier version of the Wolfram Language.
BUILT-IN WOLFRAM LANGUAGE SYMBOL

ImageDistance

ImageDistance[image1,image2]
returns a distance measure between and .

ImageDistance[image1,image2,pos]
places the center of at position pos in .

ImageDistance[image1,image2,pos1,pos2]
places the point of at position in .

Details and OptionsDetails and Options

• ImageDistance[image1,image2] centers in 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 or 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 Center center 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:
•  DistanceFunction EuclideanDistance distance function to use Masking All region of interest
• Some typical distance function settings include:
•  EuclideanDistance Euclidean distance (default) SquaredEuclideanDistance squared Euclidean distance NormalizedSquaredEuclideanDistance normalized squared Euclidean distance ManhattanDistance Manhattan or "city block" distance CosineDistance angular cosine distance CorrelationDistance correlation coefficient distance f function 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 {"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 , where are the values and are the variance of the spatial derivatives along dimension s of {"EarthMoverDistance",n} earth mover distance using n-bin histogram (default )
• Using Masking->roi, a region of interest in is specified. With Masking->{roi1,roi2}, the intersection of and 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. »

ExamplesExamplesopen allclose all

Basic Examples  (2)Basic Examples  (2)

Euclidean distance between two images:

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Euclidean distance between 3D images:

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