Analysis module

#include "diplib/analysis.h"

struct dip::SubpixelLocationResult

Contains the result of the function dip::SubpixelLocation.

using dip::SubpixelLocationArray = std::vector<SubpixelLocationResult>

Contains the result of the functions dip::SubpixelMaxima and dip::SubpixelMinima.

dip::CoordinateArray dip::Find( dip::Image const& in, dip::Image const& mask = {})

Finds the coordinates for all non-zero pixels in the scalar image in, optionally constrained to the pixels selected by mask.

in must be scalar, but can have any data type. mask, if forged, must be of the same sizes as in, or be singleton expandable to that size, and must be binary.

The output array contains the coordinates for all non-zero pixels, in linear index order (that is, the image is traversed row-wise to find the pixels). Note that this function is intended to be used with images that have relatively few non-zero pixels, and there usually is a better alternative than to list the coordinates of all non-zero pixels.

dip::SubpixelLocationResult dip::SubpixelLocation( dip::Image const& in, dip::UnsignedArray const& position, dip::String const& polarity = S::MAXIMUM, dip::String const& method = S::PARABOLIC_SEPARABLE)

Gets coordinates of a local extremum with sub-pixel precision

Determines the sub-pixel location of a local maximum or minimum close to position. position should point to a pixel whose value is larger than its direct neighbors’ (if polarity is "maximum") or smaller than its direct neighbors’ (polarity is "minimum").

The coordinates member of the output struct will contain the the sub-pixel location of this local extremum. The value member will contain the interpolated grey value at the location of the extremum.

method determines which method is used. These are the allowed values:

• "linear": Computes the center of gravity of 3 pixels around the extremum, in each dimension independently. The value at the extremum is that of the pixel at position.
• "parabolic": Fits a parabola to a 3-pixel-wide block around the extremum (for up to 3D only).
• "parabolic separable": Fits a parabola in each dimension independently. The value at the extremum is the maximum/minimum of the values obtained in each of the 1D processes, and thus not equivalent to the grey value obtained by true interpolation.
• "gaussian": Fits a Gaussian to a 3-pixel-wide block around the extremum (for up to 3D only).
• "gaussian separable": Fits a Gaussian in each dimension independently. The value at the extremum is the maximum/minimum of the values obtained in each of the 1D processes, and thus not equivalent to the grey value obtained by true interpolation.
• "integer": Doesn’t look for sub-pixel locations, returning the integer coordinates of the extremum.

The image in must be scalar and real-valued.

dip::SubpixelLocationArray dip::SubpixelMaxima( dip::Image const& in, dip::Image const& mask = {}, dip::String const& method = S::PARABOLIC_SEPARABLE)

Gets coordinates of local maxima with sub-pixel precision

Detects local maxima in the image, and returns their coordinates, with sub-pixel precision. Only pixels where mask is on will be examined. Local maxima are detected using dip::Maxima, then their position is determined more precisely using dip::SubpixelLocation.

A local maximum can not touch the edge of the image. That is, its integer location must be one pixel away from the edge.

See dip::SubpixelLocation for the definition of the method parameter.

dip::SubpixelLocationArray dip::SubpixelMinima( dip::Image const& in, dip::Image const& mask = {}, dip::String const& method = S::PARABOLIC_SEPARABLE)

Gets coordinates of local minima with sub-pixel precision

Detects local minima in the image, and returns their coordinates, with sub-pixel precision. Only pixels where mask is on will be examined. Local minima are detected using dip::Minima, then their position is determined more precisely using dip::SubpixelLocation.

A local minimum can not touch the edge of the image. That is, its integer location must be one pixel away from the edge.

See dip::SubpixelLocation for the definition of the method parameter.

dip::FloatArray dip::MeanShift( dip::Image const& meanShiftVectorResult, dip::FloatArray const& start, dip::dfloat epsilon = 1e-3)

Finds the coordinates of a local maximum close to start

The mean shift method iteratively finds the center of gravity of a Gaussian-shaped neighborhood around start, moving start to this location, until convergence. The location that the process converges to is a local maximum. Convergence is assumed when the magnitude of the shift is smaller than epsilon.

To speed up the computation within this function, the output of dip::MeanShiftVector is used. This filter pre-computes the center of gravity of all neighborhoods in the image. Specify the neighborhood sizes in that function.

dip::FloatCoordinateArray dip::MeanShift( dip::Image const& meanShiftVectorResult, dip::FloatCoordinateArray const& startArray, dip::dfloat epsilon = 1e-3)

Finds the coordinates of local a maximum close to each point in startArray.

Repeatedly calls the dip::MeanShift function described above, for each point in startArray. Duplicate maxima are not removed, such that out[ii] is the local maximum arrived to from startArray[ii].

void dip::GaussianMixtureModel( dip::Image const& in, dip::Image& out, dip::uint dimension = 2, dip::uint numberOfGaussians = 2, dip::uint maxIter = 20, dip::StringSet const& flags = {})

Determines the parameters for a Gaussian Mixture Model for every line in the image.

in must be real-valued and scalar. For each image line along dimension, a Gaussian Mixture Model (GMM) is fitted to find the numberOfGaussians most important Gaussian components. In the output image, dimension indexes the Gaussian component. The components are stored in order of importance, the component with the largest amplitude first. The parameters to each Gaussian are stored as tensor components, in the order position, amplitude and sigma.

out is a double-precision floating-point image with three tensor components. It has a size of numberOfGaussians along dimension, and the same size as in along all other dimensions.

maxIter sets how many iterations are run. There is currently no other stopping criterion.

flags can contain the following values:

• "periodic" specifies that the image is periodic along dimension.
• "pixel size" scales the parameters written to the output image with the pixel size along dimension. By default these parameters are in pixels.

void dip::CrossCorrelationFT( dip::Image const& in1, dip::Image const& in2, dip::Image& out, dip::String const& in1Representation = S::SPATIAL, dip::String const& in2Representation = S::SPATIAL, dip::String const& outRepresentation = S::SPATIAL, dip::String const& normalize = S::NORMALIZE)

Calculates the cross-correlation between two images of equal size.

The normalize flag determines whether to compute the regular cross-correlation ("don't normalize") or to normalize the frequency-domain inputs so that only the phase information is of importance ("normalize" or "phase"). The normalization results as a very sharp peak in the spatial domain, which increases the precision with which a shift between the two images can be determined. Note that this normalization is not related to what is commonly referred to as “normalized cross-correlation”, where the input images are whitened (in the spatial domain) before the cross-correlations is computed. The method is instead related to the “phase correlation” as proposed by Kuglin and Hines (1975).

When normalize is "normalize", the computation performed in the Fourier Domain is (as per Luengo & van Vliet, 2000)

out = ( in1 * dip::Conjugate(in2) ) / dip::SquareModulus(in1);

When normalize is set to "phase", the computation is (as per Kuglin & Hines, 1975)

out = ( in1 * dip::Conjugate(in2) ) / ( dip::Modulus(in1) * dip::Modulus(in2) );

(See dip::Conjugate, dip::SquareModulus and dip::Modulus.)

These two approaches are identical if the only difference between in1 and in2 is a shift, except that "normalize" is computationally cheaper than "phase". If the images are otherwise not identical, then the "phase" method might be the better choice. If there exist strong differences, the non-normalized method can be the best option.

As elsewhere, the origin is in the middle of the image, on the pixel to the right of the center in case of an even-sized image. Thus, for in1==in2, only this pixel will be set. See dip::FindShift with the "CC", "NCC" or "PC" methods for localizing this peak.

If in1 or in2 is already Fourier transformed, set in1Representation or in2Representation to "frequency". Similarly, if outRepresentation is "frequency", the output will not be inverse-transformed, so will be in the frequency domain.

in1 and in2 must be scalar images with the same dimensionality and sizes. Spatial-domain images must be real-valued, and frequency-domain images are assumed (but not tested) to be conjugate-symmetric (i.e. be Fourier transforms of real-valued images). out will be real-valued if outRepresentation is "spatial".

void dip::AutoCorrelationFT( dip::Image const& in, dip::Image& out, dip::String const& inRepresentation = S::SPATIAL, dip::String const& outRepresentation = S::SPATIAL)

Computes the auto-correlation function.

Equivalent to calling dip::CrossCorrelationFT with the same image as the two inputs, and the normalize flag set to "don't normalize" (normalization would yield a very boring output indeed).

As elsewhere, the origin is in the middle of the image, on the pixel to the right of the center in case of an even-sized image.

If in is already Fourier transformed, set inRepresentation to "frequency". Similarly, if outRepresentation is "frequency", the output will not be inverse-transformed, so will be in the frequency domain.

in must be a scalar image. Spatial-domain images must be real-valued, and a frequency-domain image is assumed (but not tested) to be conjugate-symmetric (i.e. the Fourier transform of real-valued image). out will be real-valued if outRepresentation is "spatial".

dip::FloatArray dip::FindShift( dip::Image const& in1, dip::Image const& in2, dip::String const& method = S::MTS, dip::dfloat parameter = 0, dip::UnsignedArray maxShift = {})

Estimates the (sub-pixel) global shift between in1 and in2.

The numbers found represent the shift of in2 with respect to in1, or equivalently, the position of the top-left pixel of in2 in the coordinate system of in1. Shifting in1 by the returned shift aligns the two images. See dip::Shift.

There are various methods that can be used to determine the shift, see below. For the methods that require that the shift be small, first the integer pixel is calculated using cross correlation, and both images are cropped to the common part. maxShift can be used to restrict the shift found. This is useful, for example, when the images contain a repeating pattern. Noise in the images can cause a shift of several pattern periods to be “optimal” (e.g. the cross-correlation yields a larger value), even if a much smaller shift would also explain the differences. In this case, set maxShift to be slightly smaller than the pattern period along each dimension.

Valid strings for method are:

• "integer only": The cross-correlation method simply computes the cross-correlation, using dip::CrossCorrelationFT, and then finds the position of the pixel with the largest value. This method works for any number of dimensions. parameter is ignored.

• "CC": The cross-correlation method computes the cross-correlation, using dip::CrossCorrelationFT, and then uses dip::SubpixelLocation to find the location of the largest peak with sub-pixel precision. This method works for any number of dimensions. parameter is ignored.

• "NCC": As "CC", but using the normalized cross-correlation, which makes the peak much sharper (Luengo Hendriks, 1998). This method works for any number of dimensions. parameter is ignored. See the notes in dip::CrossCorrelationFT regarding the normalization of the cross-correlation using the "normalize" flag, which is not as what is commonly referred to as “normalized cross-correlation”.

• "PC": As "CC", but using phase correlation. This method works for any number of dimensions. parameter is ignored. See the notes in dip::CrossCorrelationFT regarding the normalization of the cross-correlation using the "phase" flag.

• "CPF": The CPF method (see Luengo Hendriks (1998), where it is called FFTS) uses the phase of the cross-correlation (as calculated by dip::CrossCorrelationFT) to estimate the shift. parameter sets the largest frequency used in this estimation. The maximum value that makes sense is sqrt(0.5). Any larger value will give the same result. Choose smaller values to ignore the higher frequencies, which have a smaller SNR and are more affected by aliasing. If parameter is <= 0, the optimal found for images sub-sampled by a factor four will be used (parameter = 0.2). This method only supports 2-D images.

• "MTS": The MTS method (see Luengo Hendriks (1998), where it is called GRS) uses a first order Taylor approximation of the equation in1(t) = in2(t-s) at scale parameter. If parameter is <= 0, a scale of 1 will be used. This means that the images will be smoothed with a Gaussian kernel of 1. This method is more accurate than CPF. This method supports images with a dimensionality between 1 and 3.

• "ITER": The ITER method is an iterative version of the MTS method. It is known that a single estimation with MTS has a bias due to truncation of the Taylor expansion series (Pham et al., 2005). The bias can be expressed as a polynomial of the subpixel displacements. As a result, if the MTS method is applied iteratively, and the shift is refined after each iteration, the bias eventually becomes negligible. By using just 3 iterations, and noticing that log(bias_increment) is a linear sequence, it is possible to correct for the bias up to O(10⁶).

Set parameter to 0 for normal behavior. parameter in the range (0,0.1] specifies the desired accuracy. A negative parameter causes round(-parameter) iterations to be run. This method supports images with a dimensionality between 1 and 3.

• "PROJ": The PROJ method computes the shift in each dimension separately, applying the ITER method on the projection of the image onto each axis. It is fast and fairly accurate for high SNR. Should not be used for low SNR. parameter is passed unchanged to the ITER method. This method supports images with any number of dimensions.

in1 and in2 must be scalar images with the same dimensionality and sizes.

dip::FloatArray dip::FourierMellinMatch2D( dip::Image const& in1, dip::Image const& in2, dip::Image& out, dip::String const& interpolationMethod = S::LINEAR, dip::String const& correlationMethod = S::PHASE)

Finds the scaling, translation and rotation between two 2D images using the Fourier Mellin transform

The output array represents a 2x3 affine transform matrix (in column-major order) that can be used as input to dip::AffineTransform to transform in2 to match in1. out is the in2 image transformed in this way.

The two images must be real-valued, scalar and two-dimensional, and have the same sizes.

interpolationMethod has a restricted set of options: "linear", "3-cubic", or "nearest". See Interpolation methods for their definition. If in is binary, interpolationMethod will be ignored, nearest neighbor interpolation will be used.

correlationMethod determines the normalization applied in the cross correlation. It can be "don't normalize", "normalize" or "phase". See dip::CrossCorrelationFT for an explanation of these flags.

Example:

dip::Image in1, in2, out;
// assign to in1 and in2 two similar images
auto T = FourierMellinMatch2D( in1, in2, out );
// out is a transformed version of in2, as per
dip::AffineTransform( in2, out, T );

void dip::StructureTensor( dip::Image const& in, dip::Image const& mask, dip::Image& out, dip::FloatArray const& gradientSigmas = {1.0}, dip::FloatArray const& tensorSigmas = {5.0}, dip::String const& method = S::BEST, dip::StringArray const& boundaryCondition = {}, dip::dfloat truncation = 3)

Computes the structure tensor.

The structure tensor is a tensor image that contains, at each pixel, information about the local image structure. The eigenvalues of the structure tensor are larger when there are stronger gradients locally. That is, if all eigenvalues are small, the image is locally uniform. If one eigenvalue is large, then there is a unique line or edge orientation (in 2D), or a plane-like edge or structure (in 3D). In 3D, if two eigenvalues are large then there is a line-like structure. The associated eigenvalues indicate the orientation of this structure. See the literature references below for more information.

in must be a scalar, real-valued image. out will be a symmetric NxN tensor image, where N is the number of dimensions in in. Out is computed by:

dip::Image g = dip::Gradient( in, gradientSigmas );
dip::Image out = dip::Gauss( g * dip::Transpose( g ), tensorSigmas );

If mask is given (not a raw image), then it is interpreted as confidence weights for the input pixels. It should have values between 0 and 1 (or be binary). Normalized differential convolution is used to compute the gradients. This method applies Gaussian gradients taking missing and uncertain values into account. See dip::NormalizedDifferentialConvolution.

See dip::Gauss for the meaning of the parameters method, boundaryCondition and truncation.

The functions dip::StructureTensorAnalysis2D and dip::StructureTensorAnalysis3D can be used with the output of this function to obtain useful image parameters.

void dip::StructureTensorAnalysis2D( dip::Image const& in, dip::Image* l1 = nullptr, dip::Image* l2 = nullptr, dip::Image* orientation = nullptr, dip::Image* energy = nullptr, dip::Image* anisotropy1 = nullptr, dip::Image* anisotropy2 = nullptr, dip::Image* curvature = nullptr)

Computes useful image parameters from the 2D structure tensor.

in must be a 2D, symmetric 2x2 tensor image obtained from dip::StructureTensor. This function takes a pointer to output images, instead of taking them by reference. Set pointers to nullptr if you do not want the given output computed. Use this function as follows:

dip::Image st = dip::StructureTensor( img );
dip::Image energy, orientation;
dip::StructureTensorAnalysis2D( st, nullptr, nullptr, &orientation, &energy );

(note how the last two parameters were not given, they default to nullptr). The code above computes both the orientation and energy values of the structure tensor.

The output images will be reallocated to be the same size as the input image. They will be scalar and of a floating-point type.

The output images are defined as follows:

Curvature sign cannot be computed because the structure tensor does not distinguish the direction of the gradient.

Note that l1 and l2 will both reference data within the same data segment, and therefore will likely not have normal strides.

For a 3D structure tensor analysis, see the function dip::StructureTensorAnalysis3D. A different interface to this function is available in dip::StructureTensorAnalysis. Note that eigenvalues and eigenvectors can also be computed using dip::Eigenvalues and dip::EigenDecomposition.

void dip::StructureTensorAnalysis3D( dip::Image const& in, dip::Image* l1 = nullptr, dip::Image* phi1 = nullptr, dip::Image* theta1 = nullptr, dip::Image* l2 = nullptr, dip::Image* phi2 = nullptr, dip::Image* theta2 = nullptr, dip::Image* l3 = nullptr, dip::Image* phi3 = nullptr, dip::Image* theta3 = nullptr, dip::Image* energy = nullptr, dip::Image* cylindrical = nullptr, dip::Image* planar = nullptr)

Computes useful image parameters from the 3D structure tensor.

in must be a 3D, symmetric 3x3 tensor image obtained from dip::StructureTensor. This function takes a pointer to output images, instead of taking them by reference. Set pointers to nullptr if you do not want the given output computed. Use this function as follows:

dip::Image st = dip::StructureTensor( img );
dip::Image energy;
dip::StructureTensorAnalysis3D( st, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, &energy );

(note how the last two parameters were not given, they default to nullptr). The code above computes the energy value of the structure tensor.

The output images will be reallocated to be the same size as the input image. They will be scalar and of a floating-point type.

The output images are defined as follows:

For a 2D structure tensor analysis, see the function dip::StructureTensorAnalysis2D. A different interface to this function is available in dip::StructureTensorAnalysis. Note that eigenvalues and eigenvectors can also be computed using dip::Eigenvalues and dip::EigenDecomposition.

void dip::StructureTensorAnalysis( dip::Image const& in, dip::ImageRefArray& out, dip::StringArray const& outputs)

Interface to dip::StructureTensorAnalysis2D and dip::StructureTensorAnalysis3D.

in is as in dip::StructureTensorAnalysis2D or dip::StructureTensorAnalysis3D. That is, either a 2D symmetric 2x2 tensor image or a 3D symmetric 3x3 tensor image, real-valued, as obtained from dip::StructureTensor. out is an array with references to output images. It should have exactly as many elements as outputs.

outputs is an array with one or more of the following strings, indicating which outputs are needed:

• For 2D inputs: "l1", "l2", "orientation", "energy", "anisotropy1", "anisotropy2", "curvature".
• For 3D inputs: "l1", "phi1", "theta1", "l2", "phi2", "theta2", "l3", "phi3", "theta3", "energy", "cylindrical", "planar".

The order of the strings in outputs indicates the order they will be written to the out array.

See the functions dip::StructureTensorAnalysis2D and dip::StructureTensorAnalysis3D for more information on these outputs.

dip::Distribution dip::StructureAnalysis( dip::Image const& in, dip::Image const& mask, std::vector<dfloat> const& scales = {}, dip::String const& feature = "energy", dip::FloatArray const& gradientSigmas = {1.0}, dip::String const& method = S::BEST, dip::StringArray const& boundaryCondition = {}, dip::dfloat truncation = 3)

Analyzes the local structure of the image at multiple scales.

Computes the structure tensor, smoothed with each of the values in scales multiplied by gradientSigmas, determines the feature feature from it, and averages across the image for each scale. This leads to a series of data points showing how the selected feature changes across scales. scales and gradientSigmas are given in pixels, the image’s pixel size is not taken into account.

The reason that each element of scales is multiplied by gradientSigmas is to allow analysis of non-isotropic images. The gradientSigmas corrects for the non-isotropy, allowing scales to be a scalar value for each scale.

scales defaults to a series of 10 values geometrically spaced by sqrt(2), and starting at 1.0. Units are pixels.

feature can be any of the strings allowed by dip::StructureTensorAnalysis, but "energy", "anisotropy1", and "anisotropy2" (in 2D), and "energy", "cylindrical", and "planar" (in 2D) make the most sense.

See dip::Gauss for the meaning of the parameters method, boundaryCondition and truncation.

in must be scalar and real-valued, and either 2D or 3D. mask must be of the same size, and limits the region over which the structure tensor feature is averaged.

See dip::StructureTensor for more information about the structure tensor.

void dip::MonogenicSignal( dip::Image const& in, dip::Image& out, dip::FloatArray const& wavelengths = {3.0,24.0}, dip::dfloat bandwidth = 0.41, dip::String const& inRepresentation = S::SPATIAL, dip::String const& outRepresentation = S::SPATIAL)

Computes the monogenic signal, a multi-dimensional generalization of the analytic signal.

The monogenic signal of an n-dimensional image has n+1 components. The first component has been filtered with the even (symmetric) filter component, and the other n components have been filtered with the odd (antisymmetric) filter components, where each of those filter components is antisymmetric along a different dimension.

This function splits the frequency spectrum of the image into wavelengths.size() components. The radial component of a log-Gabor filter bank will be used. These radial frequency filters are defined by wavelengths (in pixels) and bandwidth. See dip::LogGaborFilterBank for details.

The filters are always applied in the frequency domain. If outRepresentation is "spatial", the inverse Fourier transform will be applied to bring the result back to the spatial domain. Otherwise, it should be "frequency", and no inverse transform will be applied. Likewise, inRepresentation specifies whether in has already been converted to the frequency domain or not.

in must be scalar and real-valued if given in the spatial domain. If in is in the frequency domain, it is expected to be conjugate symmetric, and thus have a real-valued inverse transform (the imaginary part of the inverse transform will be discarded). Out will be a tensor image with wavelengths.size() tensor columns and n+1 tensor rows. The data type will be single-precision float for spatial-domain output, or single-precision complex for frequency-domain output.

void dip::MonogenicSignalAnalysis( dip::Image const& in, dip::ImageRefArray& out, dip::StringArray const& outputs, dip::dfloat noiseThreshold = 0.2, dip::dfloat frequencySpreadThreshold = 0.5, dip::dfloat sigmoidParameter = 10, dip::dfloat deviationGain = 1.5, dip::String const& polarity = S::BOTH)

Computes useful image parameters from the monogenic signal.

in is a tensor image produced by dip::MonogenicSignal, for which in.TensorRows() == in.Dimensionality() + 1, and in.TensorColumns() > 1. In the case of a single column (a single wavelength was given), the congruency output is not possible. in is real-valued (single-precision float) and in the spatial domain. out is an array with references to output images. It should have exactly as many elements as outputs.

outputs is an array with one or more of the following strings, indicating which outputs are needed:

The order of the strings in outputs indicates the order they will be written to the out array.

The output images will be reallocated to be the same size as the input image. They will be scalar and of a floating-point type.

noiseThreshold indicates the noise threshold to use on the energy images when computing phase congruency or symmetry.

Two different algorithms for phase congruency are implemented:

• Kovesi’s method works for images in 2D only, and requires several scales (>2) to be present in the monogenic signal. This method will use the following input arguments:

  • frequencySpreadThreshold is a threshold that avoids high phase congruency values if the frequency spread is not large enough.
  • sigmoidParameter is the parameter to the sigmoid function that weighs congruency with the frequency spread.
  • deviationGain determines how much the calculated phase deviation should be magnified. Larger values create a sharper response to edges, but also reduces this response’s magnitude. Sensible values are in the range [1,2].

• Felsberg’s method works in any number of dimensions, and requires exactly two scales to be present in the monogenic signal. This method ignores the three input arguments specified above.

Phase symmetry can be computed with any number of dimensions and any number of scales. The polarity input argument will be used. It is the string "white", "black" or "both", indicating which symmetry features to find.

The other outputs are computed as part of the computation of phase congruency or phase symmetry. These outputs can be requested without requesting the two main parameters, but might require some of the input arguments discussed above to be set.

void dip::OrientationSpace( dip::Image const& in, dip::Image& out, dip::uint order = 8, dip::dfloat radCenter = 0.1, dip::dfloat radSigma = 0.8, dip::uint orientations = 0)

Creates an orientation space for a 2D image

out is a 3D image, where the 3^rd dimension represents orientation. Structures (lines and edges) in the image are separated into different orientation planes. This transformation separates, for example, intersecting rings (such as the logo of the Olympic Games) into non-touching objects. This is accomplished using an orientation-selective quadrature filter. Because it’s a quadrature filter, the output is complex-valued, with the real component responding to lines and the imaginary component responding to edges.

order determines the angular selectivity. Increasing this number makes the filter longer, and therefore it responds to a narrower range of orientations. radCenter determines the overall scaling of the filter. Increasing this value makes the filter smaller. The radSigma parameter controls the bandwidth of the filter. Reducing this value makes the filter more specific to a frequency range (the scale selected by radCenter).

By default, the orientation space is sampled at order * 2 + 1 different orientations (this is the number of filters applied, and the number of samples along the 3^rd dimension). orientation, when positive, determines the number of orientation used. This parameter can be used to oversample the orientation space.

dip::Distribution dip::PairCorrelation( dip::Image const& object, dip::Image const& mask, dip::Random& random, dip::uint probes = 1000000, dip::uint length = 100, dip::String const& sampling = S::RANDOM, dip::StringSet const& options = {})

Estimates the pair correlation function of the different phases in object.

If object is a binary image, the image is a regarded as a two-phase image. In case object is of an unsigned integer type, the image is regarded as a labeled image, with each integer value encoding a phase (note that 0 is included here).

Optionally a mask image can be provided to select which pixels in object should be used to estimate the pair correlation.

probes specifies how many random point pairs should be drawn to estimate the function. length specifies the maximum correlation length in pixels. The correlation function can be estimated using a random sampling method (sampling is "random"), or a grid sampling method ("grid"). For grid sampling, probes can be 0, in which case all possible pairs along all image axes are used. Otherwise, the grid covers a subset of points, with all possible pairs along all image axes; the actual number of pairs is approximate. In general, the grid method needs a lot more probes to be precise, but it is faster for a given number of probes because it uses sequential memory access.

options can contain one or more of the following strings:

• "covariance": Compute covariance instead of correlation.
• "normalize volume": Normalizes the distribution using the volume fraction
• "normalize volume^2": Normalizes the distribution using the square of the volume fraction

Without any of the option strings given, the output has N values, with N the number of phases in the image. Element i gives the probability of two points at a given distance both hitting phase number i. When normalized using the "normalize volume" option, the probability given is that of the second point hitting phase i, given that the first point hits phase i. This normalization is computed by dividing all values (distances) for element i by the volume fraction of phase i (which is given by the estimated probability at distance equal to 0). When normalized using the "normalize volume^2" option, the values given are expected to tends towards 1, as the square of the volume fraction of phase i is the probability of two random points hitting phase i.

These values are read from the output distribution by distribution[distance].Y(i), and the corresponding distance in physical units is given by distribution[distance].X(). If object does not have a pixel size, its pixel sizes are not isotropic, or have no physical units, then the distances given are in pixels. distance runs from 0 to length (inclusive).

With the "covariance" option, the output distribution has N×N values. The element at (i,j) gives the probability for two points at a given distance to land on phases i and j respectively. The value for (i,j) will be identical to that for (j,i). The values at the diagonal will correspond to the pair correlation as described above. The rest of the elements are for the cases where the two points fall in different phases. Normalization breaks the symmetry, since each column i is normalized by the volume fraction for phase i. That is, (i,j) will give the probability that the second point, at a given distance, will hit phase j given that the first point hits phase i.

dip::Distribution dip::PairCorrelation( dip::Image const& object, dip::Image const& mask, dip::uint probes = 1000000, dip::uint length = 100, dip::String const& sampling = S::RANDOM, dip::StringSet const& options = {})

like above, using a default-initialized dip::Random object.

dip::Distribution dip::ProbabilisticPairCorrelation( dip::Image const& phases, dip::Image const& mask, dip::Random& random, dip::uint probes = 1000000, dip::uint length = 100, dip::String const& sampling = S::RANDOM, dip::StringSet const& options = {})

Estimates the probabilistic pair correlation function of the different phases in phases.

phases is a real-valued image of a floating-point type, with one or more channels (tensor elements). Each channel represents the probability per pixel of one of the phases. The function assumes, but does not check, that these values are with the [0,1] range, and add up to 1 or less.

All other parameters are as in dip::PairCorrelation.

dip::Distribution dip::ProbabilisticPairCorrelation( dip::Image const& object, dip::Image const& mask, dip::uint probes = 1000000, dip::uint length = 100, dip::String const& sampling = S::RANDOM, dip::StringSet const& options = {})

like above, using a default-initialized dip::Random object.

dip::Distribution dip::Semivariogram( dip::Image const& in, dip::Image const& mask, dip::Random& random, dip::uint probes = 1000000, dip::uint length = 100, dip::String const& sampling = S::RANDOM)

Estimates the expected value of half the square difference between field values at a distance d.

The input image in must be scalar and real-valued. An isotropic semivariogram is computed as a function of the distance, which is varied from 0 to length (in pixels). Therefore, the field in in is assumed isotropic and stationary.

By definition, the semivariogram is zero at the origin (at zero distance). If there is no spatial correlation, the semivariogram is constant for non-zero distance. The magnitude of the jump at the origin is called nugget. The value of the semivariogram in the limit of the distance to infinity is the variance of the field; this value is called the sill. The distance at which the sill is reached (or, more robustly, the distance at which 0.95 times the sill is reached) is called range, and provides a meaningful description of the field.

Optionally a mask image can be provided to select which pixels in in should be used to estimate the semivariogram.

probes specifies how many random point pairs should be drawn to estimate the semivariogram. length specifies the maximum pair distance in pixels. The semivariogram can be estimated using a random sampling method (sampling is "random"), or a grid sampling method ("grid"). For grid sampling, probes can be 0, in which case all possible pairs along all image axes are used. Otherwise, the grid covers a subset of points, with all possible pairs along all image axes; the actual number of pairs is approximate. In general, the grid method needs a lot more probes to be precise, but it is faster for a given number of probes because it uses sequential memory access.

The output dip::Distribution has x samples given in physical units. However, if in does not have a pixel size, its pixel sizes are not isotropic, or have no physical units, then the distances given are in pixels.

dip::Distribution dip::Semivariogram( dip::Image const& object, dip::Image const& mask, dip::uint probes = 1000000, dip::uint length = 100, dip::String const& sampling = S::RANDOM)

like above, using a default-initialized dip::Random object.

dip::Distribution dip::ChordLength( dip::Image const& object, dip::Image const& mask, dip::Random& random, dip::uint probes = 100000, dip::uint length = 100, dip::String const& sampling = S::RANDOM)

Estimates the chord length distribution of the different phases in object.

If object is a binary image, the image is a regarded as a two-phase image. In case object is of an unsigned integer type, the image is regarded as a labeled image, with each integer value encoding a phase (note that 0 is included here).

Optionally a mask image can be provided to select which pixels in object should be used for the estimation.

probes specifies how many random lines should be drawn to estimate the distribution. length specifies the maximum chord length in pixels. The chord length distribution can be estimated using a random sampling method (sampling is "random"), or a grid sampling method ("grid"). For grid sampling, probes can be 0, in which case all lines along all image axes are used. Otherwise, the grid covers a subset of lines; the actual number of lines is approximate. In general, the grid method needs a lot more probes to be precise, but it is faster for a given number of probes because it uses sequential memory access.

The output has N values, with N the number of phases in the image. Element i gives the probability of a chord of length len pixels within phase number i. The chord length is the length of an intersection of a line with the phase. These values are read from the output distribution by distribution[len].Y(i), and the corresponding length in physical units is given by distribution[len].X(). If object does not have a pixel size, its pixel sizes are not isotropic, or have no physical units, then the distances given are in pixels. len runs from 0 to length (inclusive).

dip::Distribution dip::ChordLength( dip::Image const& object, dip::Image const& mask, dip::uint probes = 100000, dip::uint length = 100, dip::String const& sampling = S::RANDOM)

like above, using a default-initialized dip::Random object.

dip::Distribution dip::DistanceDistribution( dip::Image const& object, dip::Image const& region, dip::uint length = 100)

Computes the distribution of distances to the background of region for the different phases in object.

If object is a binary image, the image is a regarded as a two-phase image. In case object is of an unsigned integer type, the image is regarded as a labeled image, with each integer value encoding a phase (note that 0 is included here).

The region image is a binary image, the distances to its background will be computed. If region is a labeled image, the distances to the zero-valued pixels will be computed. These distances will take the pixels sizes of region into account. If region does not have pixels sizes, those of object will be used instead.

The output dip::Distribution has x value increments given by the pixel size. That is, the distribution has one sample per pixel. length is the length of the distribution, and thus limits the distances taken into account.

dip::Distribution dip::Granulometry( dip::Image const& in, dip::Image const& mask, std::vector<dfloat> const& scales = {}, dip::String const& type = "isotropic", dip::String const& polarity = S::OPENING, dip::StringSet const& options = {})

Computes the granulometric function for an image

The granulometry yields a volume-weighted, grey-value–weighted, cumulative distribution of object sizes. It can be used to obtain a size distribution of the image without attempting to segment and separate individual objects. It is computed by a series of openings or closings at different scales. The result at each scale is integrated (summed). The obtained series of values is scaled such that the value for scale 0 is 0, and for scale infinity is 1.

The derivative of the cumulative distribution is a volume-weighted and grey-value–weighted distribution of object sizes. See dip::Distribution::Differentiate.

Grey-value–weighted means that objects with a larger grey-value contrast will be weighted more heavily. Ensuring a uniform grey-value contrast to prevent this characteristic from affecting the estimated size distribution.

Volume-weighted means that objects are weighted by their volume (area in 2D). By dividing the distribution (note: not the cumulative distribution) by the volume corresponding to each scale, it is possible to approximate a count-based distribution.

This function implements various granulometries, to be specified through the parameters type, polarity and options. The following type values specify the shapes of the structuring element (SE), which determines the measurement type:

• "isotropic": An isotropic SE leads to a size distribution dictated by the width of objects.
• "length": A line SE leads to a size distribution dictated by the length of objects. We use (constrained) path openings or closings (see Luengo, 2010).

The polarity flag determines whether it is white objects on a black background ("opening") or black objects on a white background ("closing") that are being analyzed.

The options parameter can contain a set of flags that modify how the operations are applied. The allowed flags differ depending on the type flag.

• For "isotropic" granulometries:

  • "reconstruction": uses openings or closings by reconstruction instead of structural openings or closings. This leads to objects not being broken up in the same way. Objects need to be clearly separated spatially for this to work.
  • "shifted": uses sub-pixel shifted isotropic structuring elements. This allows a finer sampling of the scale axis (see Luengo et al., 2007). Ignored for images with more than 3 dimensions.
  • "interpolate": interpolates by a factor up to 8x for smaller scales, attempting to avoid SE diameters smaller than 8. This improves precision of the result for small scales (see Luengo et al., 2007).
  • "subsample": subsamples for larger scales, such that the largest SE diameter is 64. This speeds up computation, at the expense of precision.

• For "length" granulometries:

  • "unconstrained": by default, we use constrained path openings or closings, which improves the precision of the measurement, but is a little bit more expensive (see Luengo, 2010). This option causes the use of normal path openings or closings.
  • "robust": applies path openings or closings in such a way that they are less sensitive to noise.

scales defaults to a series of 12 values geometrically spaced by sqrt(2), and starting at sqrt(2). scales are in pixels, the image’s pixel size is not taken into account.

in must be scalar and real-valued. mask must have the same sizes, and limits the region in which objects are measured.

dip::dfloat dip::FractalDimension( dip::Image const& in, dip::dfloat eta = 0.5)

Estimates the fractal dimension of the binary image in the sliding box method.

The sliding box method is an enhancement of the classical box counting method that counts many more boxes at each scale, and therefore is more precise. By sliding the box one pixel at a time, it is also not affected by partial boxes (i.e. the boxes at the right and bottom edge of the image that do not fit within the image domain). The counts are computed in an efficient manner, which makes it similar in complexity to counting only the set of tessellating boxes.

The smallest scale used is a box size of 1, and the largest scale is at most half the smallest image size (i.e. min(width,height)/2. In between, scales grow exponentially with a factor 1+eta. Thus, if eta is 1, then each scale uses boxes double the size of the previous scale, and if eta is smaller then the steps are smaller and more scales are generated.

The image in must be scalar and binary, and typically is applied to an edge map of objects.

#include "diplib/distribution.h"

void dip::swap( Sample& a, Sample& b)

Swaps two samples, copying the data from other to *this, and that from *this to other. Both must have the same number of values.