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  /external/eigen/test/
eigensolver_complex.cpp 18 bool match = diffs.diagonal().sum() <= tol;
qr.cpp 82 RealScalar absdet = abs(m1.diagonal().prod());
qr_fullpivoting.cpp 104 RealScalar absdet = abs(m1.diagonal().prod());
  /external/eigen/unsupported/Eigen/src/LevenbergMarquardt/
LMpar.h 133 // we could almost use this here, but the diagonal is outside qr, in sdiag[]
  /external/eigen/unsupported/Eigen/src/Skyline/
SkylineStorage.h 16 * The diagonal elements
  /external/eigen/unsupported/Eigen/src/SparseExtra/
MatrixMarketIterator.h 91 RealScalar diag_norm = m_mat.diagonal().norm();
  /packages/apps/LegacyCamera/jni/feature_mos/src/mosaic/
Geometry.h 134 // 1) Divide the quadrilateral into two triangles by scribing a diagonal
  /packages/apps/Messaging/src/com/android/messaging/datamodel/media/
AvatarGroupRequestDescriptor.java 88 * diagonal of the overall group avatar is squareRoot(2) * w We also know that the
  /prebuilts/misc/windows/sdl2/test/
testviewport.c 58 /* Test diagonal lines */
  /packages/apps/LegacyCamera/jni/feature_stab/db_vlvm/
db_utilities_linalg.h 81 subdiagonal part in A and diagonal in d, which is 6-dimensional
95 above diagonal of A is used from the input, diagonal of A is assumed to
97 subdiagonal part in A and diagonal in d, which is n-dimensional*/
109 above diagonal of A is used from the input, diagonal of A is assumed to
111 and diagonal in d, which is 3-dimensional*/
  /external/eigen/Eigen/src/LU/
FullPivLU.h 37 * decomposition. The eigenvalues (diagonal coefficients) of U are sorted in such a way that any
151 * diagonal coefficient of U.
587 return Scalar(m_det_pq) * Scalar(m_lu.diagonal().prod());
653 * Thus, the diagonal of U ends with exactly
668 // the main diagonal. We need that to be able to apply our triangular solvers.
    [all...]
  /external/eigen/blas/f2c/
ctbmv.c 102 /* column, with the leading diagonal of the matrix in row */
103 /* ( k + 1 ) of the array, the first super-diagonal starting at */
120 /* column, with the leading diagonal of the matrix in row 1 of */
121 /* the array, the first sub-diagonal starting at position 1 in */
136 /* corresponding to the diagonal elements of the matrix are not */
ztbmv.c 102 /* column, with the leading diagonal of the matrix in row */
103 /* ( k + 1 ) of the array, the first super-diagonal starting at */
120 /* column, with the leading diagonal of the matrix in row 1 of */
121 /* the array, the first sub-diagonal starting at position 1 in */
136 /* corresponding to the diagonal elements of the matrix are not */
  /external/ImageMagick/ImageMagick/api/
morphology.html 180 <dd> FreiChen:{angle} Frei-Chen Edge Detector is based on a kernel that is similar to the Sobel Kernel, but is designed to be isotropic. That is it takes into account the distance of the diagonal in the kernel. </dd>
192 <dd> Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 | </dd>
254 <dd> Peak:radius1,radius2 Find any peak larger than the pixels the fall between the two radii. The default ring of pixels is as per "Ring". Edges Find flat orthogonal edges of a binary shape Corners Find 90 degree corners of a binary shape Diagonals:type A special kernel to thin the 'outside' of diagonals LineEnds:type Find end points of lines (for pruning a skeletion) Two types of lines ends (default to both) can be searched for Type 0: All line ends Type 1: single kernel for 4-conneected line ends Type 2: single kernel for simple line ends LineJunctions Find three line junctions (within a skeletion) Type 0: all line junctions Type 1: Y Junction kernel Type 2: Diagonal T Junction kernel Type 3: Orthogonal T Junction kernel Type 4: Diagonal X Junction kernel Type 5: Orthogonal + Junction kernel Ridges:type Find single pixel ridges or thin lines Type 1: Fine single pixel thick lines and ridges Type 2: Find two pixel thick lines and ridges ConvexHull Octagonal Thickening Kernel, to generate convex hulls of 45 degrees Skeleton:type Traditional skeleton generating kernels. Type 1: Tradional Skeleton kernel (4 connected skeleton) Type 2: HIPR2 Skeleton kernel (8 connected skeleton) Type 3: Thinning skeleton based on a ressearch paper by Dan S. Bloomberg (Default Type) ThinSE:type A huge variety of Thinning Kernels designed to preserve conectivity. many other kernel sets use these kernels as source definitions. Type numbers are 41-49, 81-89, 481, and 482 which are based on the super and sub notations used in the source research paper. </dd>
262 <dd> Chebyshev:[{radius}][x{scale}[!]] Chebyshev Distance (also known as Tchebychev or Chessboard distance) is a value of one to any neighbour, orthogonal or diagonal. One why of thinking of it is the number of squares a 'King' or 'Queen' in chess needs to traverse reach any other position on a chess board. It results in a 'square' like distance function, but one where diagonals are given a value that is closer than expected. </dd>
268 <dd> Euclidean:[{radius}][x{scale}[!]] Euclidean distance is the 'direct' or 'as the crow flys' distance. However by default the kernel size only has a radius of 1, which limits the distance to 'Knight' like moves, with only orthogonal and diagonal measurements being correct. As such for the default kernel you will get octagonal like distance function. </dd>
    [all...]
  /external/ImageMagick/www/api/
morphology.html 184 <dd> FreiChen:{angle} Frei-Chen Edge Detector is based on a kernel that is similar to the Sobel Kernel, but is designed to be isotropic. That is it takes into account the distance of the diagonal in the kernel. </dd>
196 <dd> Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 | </dd>
258 <dd> Peak:radius1,radius2 Find any peak larger than the pixels the fall between the two radii. The default ring of pixels is as per "Ring". Edges Find flat orthogonal edges of a binary shape Corners Find 90 degree corners of a binary shape Diagonals:type A special kernel to thin the 'outside' of diagonals LineEnds:type Find end points of lines (for pruning a skeletion) Two types of lines ends (default to both) can be searched for Type 0: All line ends Type 1: single kernel for 4-conneected line ends Type 2: single kernel for simple line ends LineJunctions Find three line junctions (within a skeletion) Type 0: all line junctions Type 1: Y Junction kernel Type 2: Diagonal T Junction kernel Type 3: Orthogonal T Junction kernel Type 4: Diagonal X Junction kernel Type 5: Orthogonal + Junction kernel Ridges:type Find single pixel ridges or thin lines Type 1: Fine single pixel thick lines and ridges Type 2: Find two pixel thick lines and ridges ConvexHull Octagonal Thickening Kernel, to generate convex hulls of 45 degrees Skeleton:type Traditional skeleton generating kernels. Type 1: Tradional Skeleton kernel (4 connected skeleton) Type 2: HIPR2 Skeleton kernel (8 connected skeleton) Type 3: Thinning skeleton based on a ressearch paper by Dan S. Bloomberg (Default Type) ThinSE:type A huge variety of Thinning Kernels designed to preserve conectivity. many other kernel sets use these kernels as source definitions. Type numbers are 41-49, 81-89, 481, and 482 which are based on the super and sub notations used in the source research paper. </dd>
266 <dd> Chebyshev:[{radius}][x{scale}[!]] Chebyshev Distance (also known as Tchebychev or Chessboard distance) is a value of one to any neighbour, orthogonal or diagonal. One why of thinking of it is the number of squares a 'King' or 'Queen' in chess needs to traverse reach any other position on a chess board. It results in a 'square' like distance function, but one where diagonals are given a value that is closer than expected. </dd>
272 <dd> Euclidean:[{radius}][x{scale}[!]] Euclidean distance is the 'direct' or 'as the crow flys' distance. However by default the kernel size only has a radius of 1, which limits the distance to 'Knight' like moves, with only orthogonal and diagonal measurements being correct. As such for the default kernel you will get octagonal like distance function. </dd>
    [all...]
morphology.php 180 <dd> FreiChen:{angle} Frei-Chen Edge Detector is based on a kernel that is similar to the Sobel Kernel, but is designed to be isotropic. That is it takes into account the distance of the diagonal in the kernel. </dd>
192 <dd> Type 2: Diagonal form of Kernel... | 1, sqrt(2), 0 | | sqrt(2), 0, -sqrt(2) | / 2*sqrt(2) | 0, -sqrt(2) -1 | </dd>
254 <dd> Peak:radius1,radius2 Find any peak larger than the pixels the fall between the two radii. The default ring of pixels is as per "Ring". Edges Find flat orthogonal edges of a binary shape Corners Find 90 degree corners of a binary shape Diagonals:type A special kernel to thin the 'outside' of diagonals LineEnds:type Find end points of lines (for pruning a skeletion) Two types of lines ends (default to both) can be searched for Type 0: All line ends Type 1: single kernel for 4-conneected line ends Type 2: single kernel for simple line ends LineJunctions Find three line junctions (within a skeletion) Type 0: all line junctions Type 1: Y Junction kernel Type 2: Diagonal T Junction kernel Type 3: Orthogonal T Junction kernel Type 4: Diagonal X Junction kernel Type 5: Orthogonal + Junction kernel Ridges:type Find single pixel ridges or thin lines Type 1: Fine single pixel thick lines and ridges Type 2: Find two pixel thick lines and ridges ConvexHull Octagonal Thickening Kernel, to generate convex hulls of 45 degrees Skeleton:type Traditional skeleton generating kernels. Type 1: Tradional Skeleton kernel (4 connected skeleton) Type 2: HIPR2 Skeleton kernel (8 connected skeleton) Type 3: Thinning skeleton based on a ressearch paper by Dan S. Bloomberg (Default Type) ThinSE:type A huge variety of Thinning Kernels designed to preserve conectivity. many other kernel sets use these kernels as source definitions. Type numbers are 41-49, 81-89, 481, and 482 which are based on the super and sub notations used in the source research paper. </dd>
262 <dd> Chebyshev:[{radius}][x{scale}[!]] Chebyshev Distance (also known as Tchebychev or Chessboard distance) is a value of one to any neighbour, orthogonal or diagonal. One why of thinking of it is the number of squares a 'King' or 'Queen' in chess needs to traverse reach any other position on a chess board. It results in a 'square' like distance function, but one where diagonals are given a value that is closer than expected. </dd>
268 <dd> Euclidean:[{radius}][x{scale}[!]] Euclidean distance is the 'direct' or 'as the crow flys' distance. However by default the kernel size only has a radius of 1, which limits the distance to 'Knight' like moves, with only orthogonal and diagonal measurements being correct. As such for the default kernel you will get octagonal like distance function. </dd>
    [all...]
  /external/eigen/blas/
level3_impl.h     [all...]
level2_cplx_impl.h 230 matrix(a,*n,*n,*lda).diagonal().imag().setZero();
280 matrix(a,*n,*n,*lda).diagonal().imag().setZero();
  /frameworks/native/services/inputflinger/
InputReader.h 159 // fingers are no more than this far apart relative to the diagonal size of
161 // no more than half the diagonal size of the touch pad apart.
166 // Without acceleration, a full swipe of the touch pad diagonal in movement mode
167 // will cover this portion of the display diagonal.
173 // Without acceleration, a full swipe of the touch pad diagonal in zoom mode
174 // will cover this portion of the display diagonal.
    [all...]
  /external/eigen/Eigen/src/Core/
CoreEvaluators.h 28 // It can be Dense, Sparse, Triangular, Diagonal, SelfAdjoint, Band, etc.
    [all...]
  /external/eigen/Eigen/src/Jacobi/
Jacobi.h 78 * \f$ B = \left ( \begin{array}{cc} x & y \\ \overline y & z \end{array} \right )\f$ yields a diagonal matrix \f$ A = J^* B J \f$
118 * a diagonal matrix \f$ A = J^* B J \f$
  /external/eigen/Eigen/src/QR/
HouseholderQR.h 241 return abs(m_qr.diagonal().prod());
249 return m_qr.diagonal().cwiseAbs().array().log().sum();
  /external/eigen/Eigen/src/SVD/
SVDBase.h 31 * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
32 * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
  /external/eigen/Eigen/src/SparseCore/
SparseMatrixBase.h 294 // sparse * diagonal
300 // diagonal * sparse
  /external/eigen/doc/
TutorialReductionsVisitorsBroadcasting.dox 24 The \em trace of a matrix, as returned by the function \c trace(), is the sum of the diagonal coefficients and can equivalently be computed <tt>a.diagonal().sum()</tt>.

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