Uses of Class
Facemorph.BigMat

Packages that use BigMat
Facemorph   
Facemorph.aam   
Facemorph.oesf   
Facemorph.psm   
Facemorph.tensor   
 

Uses of BigMat in Facemorph
 

Subclasses of BigMat in Facemorph
 class Gaussian
          A Gaussian distribution class
 

Methods in Facemorph that return BigMat
 BigMat PCI.combine(PCI[] pci, Template targetShape, int targetWidth, int targetHeight, Mask mask)
          Builds a PCI by combining a set of PCIs
 BigMat BigMat.copy()
          Create a copy of this matrix
 BigMat CSVFile.getAsBigMat()
           
 BigMat PCA.getComponents()
          Get the PCA components as a matrix
 BigMat PCA.getComponents(double[] average, boolean normalise)
          Gets the components as a matrix, pus 4 first rows hold linear approximations to rigid + scale parameters
 BigMat PCA.getComponents2(double[] average)
          Gets the components as a BigMat plus 4 initial cols for rigid params
 BigMat PCA.getComponents3D(double[] average, boolean normalise)
          Gets the components as a matrix, pus 7 first rows hold linear approximations to rigid + scale parameters for 3D
 BigMat Gaussian.getCovar(boolean invert)
          Uses the eigenvalues and eigenvectors to reconstruct the covariance matrix or it inverse
 BigMat PCI.getReduced(PCI pci, float[] filter, int m, Mask mask)
          Shrinks by a factor of 2 using filter to downsample
 BigMat BigMat.invertSVD(double threshold)
          find the inverse (possibly in the least squares sense) using SVD
 BigMat BigMat.multiply(BigMat m)
          Multiply this matrix by another matrix
 BigMat BigMat.multiplyOtherTranspose(BigMat m)
          Multiply the this matrix by the transpose of another matrix
 BigMat BigMat.multiplySelfTranspose(boolean transpose, boolean skipFirst)
          Multiply this matrix by the transpose of itself
 BigMat BigMat.multiplyTranspose(BigMat m)
          Multiply this matrix by another matrix
 BigMat BigMat.reconstructQR(double[] c, double[] d)
          Construct the Q and R matrices explicitly, using the output from qrdcmp R.multiply(Q) will give the original input matrix
static BigMat BigMat.removeNaNRecords(BigMat in)
          Removes any rows from the matrix that have NaNs of INF etc.
 BigMat BigMat.subMat(int[] rowIndex, int[] colIndex)
          Return a sub matrix containing only those columns points to by the indices.
 BigMat BigMat.subMat(int x, int y)
          Returns a matrix with row x and column y removed
 BigMat BigMat.subMat(int xstart, int xend, int ystart, int yend)
          Retrieve a sub-part of this matrix
 BigMat BigMat.subMatCols(int[] colIndex)
          Return a sub matrix containing only those columns pointed to by the indices.
 BigMat BigMat.subMatRows(int[] rowIndex)
          Return a sub matrix containing only those rows pointed to by the indices.
 BigMat BigMat.svbksb(double[] w, BigMat V, BigMat A, double threshold)
          SVD back substitution routine
 BigMat BigMat.svbksbt(double[] w, BigMat V, BigMat A, double threshold)
          SVD back substitution routine (transposed)
 BigMat BigMat.transposeCopy()
          Create a transposed copy of this matrix
 BigMat BigMat.upperTriangularInverseMultiply(BigMat M)
          Calculates A^(-1)M where A = this is upper triangular, by using repeated backsubstitutions.
 BigMat PCI.warp(Template targetShape, int targetWidth, int targetHeight, Mask mask)
          Warps this PCI into targetShape
 

Methods in Facemorph with parameters of type BigMat
 void BigMat.add(BigMat mat)
          Adds mat to this
static void PCA.addToAverage(Template average1, PCA pca1, Template currentAverage, int n, int start, BigMat data)
          Incrementally updates the data matrix and currentAverage by adding the PCA data to them
 int PCA.build(BigMat data)
          Builds a PCA from the data in a matrix form
 int PCA.build(BigMat data, double[] weights)
          Builds a PCA from a set of weighted samples
 boolean Gaussian.build(java.util.Vector samples, BigMat dist)
          Build a Gaussian from the supplied samples
 int PCA.buildPCAandAverage(BigMat data, double[] average)
          Builds a PCA from the data in a matrix form
 void BigMat.copy(BigMat mat)
          Copy the matrix given into this
 boolean BigMat.equals(BigMat mat)
          Checks if the two matrices are identical
 boolean BigMat.equals(BigMat mat, double delta)
          Checks if the two matrices are the same to within a given tollerance
 void Template.fitTensor(int warpType, java.awt.Image subject, java.awt.Image averageImage, Template avrg, Mask mask, Tensor shapeTensor, Tensor imageTensor, BigMat shapeLinearModel, BigMat shapeAverage, BigMat imageLinearModel, BigMat imageAverage, float scale)
          Attempt at fitting an Tensor AAM to an image using efficient reverse method, with linear projection model, includes colour information
 double[] Template.getTensorandRBparameters(BigMat shapeModel, BigMat shapeAverage, Template average, Tensor shapeTensor)
          Analyses this Template using a tensor model
 boolean BigMat.jacobi(double[] d, BigMat v, int[] nrot)
          Jacobi computes all eigenvalues and eigenvectors of a real symmetric matrix The input matrix elements above the diagonal are destroyed.
 BigMat BigMat.multiply(BigMat m)
          Multiply this matrix by another matrix
 BigMat BigMat.multiplyOtherTranspose(BigMat m)
          Multiply the this matrix by the transpose of another matrix
 BigMat BigMat.multiplyTranspose(BigMat m)
          Multiply this matrix by another matrix
 boolean BigMat.qrdcmp(BigMat Q, BigMat R)
          Decomposes this square matrix into an orthonormal matrix Q and an upper triangular matrix R such that this = R.multiply(Q)
 boolean BigMat.qrdcmp(BigMat a, double[] c, double[] d)
          From numerical recipies in C ch2.10 Constructs the QR decomposition of this matrix returning the result in a[1..n][1..n].
static BigMat BigMat.removeNaNRecords(BigMat in)
          Removes any rows from the matrix that have NaNs of INF etc.
 void PCA.solve(BigMat data)
          Builds this PCA from the data given
 void BigMat.subtract(BigMat mat)
          Subtracts mat to this
 BigMat BigMat.svbksb(double[] w, BigMat V, BigMat A, double threshold)
          SVD back substitution routine
 void BigMat.svbksb(double[] w, BigMat V, double[] b, double[] x, double threshold)
          Back substitution routine for SVD.
 BigMat BigMat.svbksbt(double[] w, BigMat V, BigMat A, double threshold)
          SVD back substitution routine (transposed)
 void BigMat.svbksbt(double[] w, BigMat V, double[] b, double[] x, double threshold)
          Back substitution routine for SVD.
 boolean BigMat.svdcmp(BigMat v, double[] w)
          Performs Singular Value Decomposition A = UwVt U replace A on output (this goes from being A to being U) v and w are filled in returns true if successful, false otherwise.
 boolean BigMat.svdcmpt(BigMat v, double[] w)
          Performs Singular Value Decomposition A = UwVt U replace A on output (this goes from being A to being U) v and w are filled in returns true if successful, false otherwise.
 BigMat BigMat.upperTriangularInverseMultiply(BigMat M)
          Calculates A^(-1)M where A = this is upper triangular, by using repeated backsubstitutions.
 

Constructors in Facemorph with parameters of type BigMat
BigMat(BigMat P)
           
BigMat(BigMat P, int x, int y)
           
Gaussian(double[] mean, BigMat covar, boolean invert)
          Construct a Gaussian using the data given
Kalman(BigMat A, BigMat P, BigMat Q, BigMat R, BigMat H, double[] x)
          Creates a new instance of Kalman
 

Uses of BigMat in Facemorph.aam
 

Methods in Facemorph.aam that return BigMat
 BigMat TaylorSolver.getV()
          Gets the matrix of principal components (derived from SVD of hessian matrix)
 

Constructors in Facemorph.aam with parameters of type BigMat
CAAMSolver(double[] f, double[][] df, BigMat hessian, double[] fdotdf, double fdotf)
          Creates a new instance of CAAMSolver
 

Uses of BigMat in Facemorph.oesf
 

Methods in Facemorph.oesf that return BigMat
static BigMat ORASM3D.toRotation(BigMat M)
          Projects M into a valid roation matrix
 

Methods in Facemorph.oesf with parameters of type BigMat
 boolean Template3D.fitTo2DOrtho(Template template, int width, int height, Vector3 centre, BigMat R, double[] T, double[] scale)
           
 void FilterSet.initialisePriors(Template3D t3d, BigMat R, double[] T, double scale, java.util.ArrayList<double[]> priorsView0, java.util.ArrayList<java.lang.Boolean> visible)
          Initialise the priors
static BigMat ORASM3D.toRotation(BigMat M)
          Projects M into a valid roation matrix
 void Template3D.transform(BigMat R, double[] T, double scale)
           
 

Uses of BigMat in Facemorph.psm
 

Methods in Facemorph.psm that return BigMat
static BigMat PSM.minimumSpanningTree(BigMat graph)
          Calculate the MST
static BigMat PSMNode.minimumSpanningTree(BigMat graph)
          Get the minimum spanning tree of the graph
 

Methods in Facemorph.psm with parameters of type BigMat
static int PSM.findBestRoot(BigMat tree, BigMat graph)
          Find the best root of the tree
static int PSMNode.findBestRoot(BigMat tree, BigMat graph)
          Finds the "best" root of the tree
static BigMat PSM.minimumSpanningTree(BigMat graph)
          Calculate the MST
static BigMat PSMNode.minimumSpanningTree(BigMat graph)
          Get the minimum spanning tree of the graph
 

Constructors in Facemorph.psm with parameters of type BigMat
PSM(int start, BigMat mst, DiagonalGMM[] appGauss, DiagonalGaussian[] gauss, boolean[] visited, java.util.ArrayList<Filter> filters, Filter lowpass, int w, int h, int levs)
          PSM constructor
PSMNode(int start, BigMat mst, Feature appGauss, DiagonalGaussian[][] gauss, boolean[] visited, int w, int h)
          Contruct a PSMNode
 

Uses of BigMat in Facemorph.tensor
 

Methods in Facemorph.tensor that return BigMat
 BigMat[] Tensor.build(boolean rotate, double[][] weights)
          build N-way model, replace this with the core tensor and return the rotation components.
 BigMat[] Tensor.build(boolean rotate, double[][] weights, int dataDim)
          build N-way model, replace this with the core tensor and return the rotation components.
 BigMat[] Tensor.buildWithAverage(double[][] weights, int dataDim, double maxvar)
          build N-way model, replace this with the core tensor and return the rotation components
 BigMat[] Tensor.buildWithAverageInPlace(double[][] weights, int dataDim, double maxvar)
          build N-way model, replace this with the core tensor and return the rotation components
static BigMat Tensor.createAverageAndSubtractMatrix(int width)
          Creates the matrix that can be used to calculate and subtract the average from rows of length width
 BigMat Tensor.getCovar(int i, boolean skipFirst)
          unfold method, unfolds along a dimension i
 BigMat Tensor.getFullLinearAnalysisMatrix(int dataDim, BigMat average, boolean skipFirst)
          Get the fll tensor as a linear analysis matrix
 BigMat Tensor.getLinearAnalysisMatrix(int dataDim, BigMat average)
          Gets a linear analysis matrix
 BigMat Tensor.planeAverages(int dim, int dataDim)
          Average across all but dim and dataDim
 BigMat Tensor.reconstructForALS(double[][] weights, int dataDim, int studyDim)
          Reconstruction for alternating least squares method
 BigMat Tensor.rowPCAMatrix(int dim, int dataDim, double[] w)
          Calculates the PCA rotation matrix along one row
 BigMat Tensor.unfold(int i)
          unfold method, unfolds along a dimension i
 

Methods in Facemorph.tensor with parameters of type BigMat
 void Tensor.fold(int i, BigMat M)
          Fold the matrix into this Tensor along the axis given
static double[] Tensor.getALSWeights(BigMat coeffs, double[] testData)
          Get the paramter weights for test data using linear matrix coeffs
 BigMat Tensor.getFullLinearAnalysisMatrix(int dataDim, BigMat average, boolean skipFirst)
          Get the fll tensor as a linear analysis matrix
 BigMat Tensor.getLinearAnalysisMatrix(int dataDim, BigMat average)
          Gets a linear analysis matrix
static double[] Tensor.linearAnalyse(BigMat linearModel, BigMat average, double[] testData)
          Analyse some test data with a linear model
 Tensor Tensor.multiply(int i, BigMat M)
          multiply method for N-mode product with M along row i
 void Tensor.multiplyInPlace(int i, BigMat M)
          multiply method for N-mode product with M along row i
 Tensor Tensor.reconstruct(BigMat[] U)
          Tensor reconstruction method
 double[] Tensor.reconstruct(double[][] weights, int i, BigMat U)
          reconstruct first multiplying by transpose U along i