Name
proj_match_points_ransacT_proj_match_points_ransacProjMatchPointsRansacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansac — Compute a projective transformation matrix between two images by
finding correspondences between points.
proj_match_points_ransac(Image1, Image2 : : Rows1, Cols1, Rows2, Cols2, GrayMatchMethod, MaskSize, RowMove, ColMove, RowTolerance, ColTolerance, Rotation, MatchThreshold, EstimationMethod, DistanceThreshold, RandSeed : HomMat2D, Points1, Points2)
Herror T_proj_match_points_ransac(const Hobject Image1, const Hobject Image2, const Htuple Rows1, const Htuple Cols1, const Htuple Rows2, const Htuple Cols2, const Htuple GrayMatchMethod, const Htuple MaskSize, const Htuple RowMove, const Htuple ColMove, const Htuple RowTolerance, const Htuple ColTolerance, const Htuple Rotation, const Htuple MatchThreshold, const Htuple EstimationMethod, const Htuple DistanceThreshold, const Htuple RandSeed, Htuple* HomMat2D, Htuple* Points1, Htuple* Points2)
Herror proj_match_points_ransac(Hobject Image1, Hobject Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HTuple& GrayMatchMethod, const HTuple& MaskSize, const HTuple& RowMove, const HTuple& ColMove, const HTuple& RowTolerance, const HTuple& ColTolerance, const HTuple& Rotation, const HTuple& MatchThreshold, const HTuple& EstimationMethod, const HTuple& DistanceThreshold, const HTuple& RandSeed, HTuple* HomMat2D, HTuple* Points1, HTuple* Points2)
HTuple HImage::ProjMatchPointsRansac(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HTuple& GrayMatchMethod, const HTuple& MaskSize, const HTuple& RowMove, const HTuple& ColMove, const HTuple& RowTolerance, const HTuple& ColTolerance, const HTuple& Rotation, const HTuple& MatchThreshold, const HTuple& EstimationMethod, const HTuple& DistanceThreshold, const HTuple& RandSeed, HTuple* Points1, HTuple* Points2) const
void ProjMatchPointsRansac(const HObject& Image1, const HObject& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HTuple& GrayMatchMethod, const HTuple& MaskSize, const HTuple& RowMove, const HTuple& ColMove, const HTuple& RowTolerance, const HTuple& ColTolerance, const HTuple& Rotation, const HTuple& MatchThreshold, const HTuple& EstimationMethod, const HTuple& DistanceThreshold, const HTuple& RandSeed, HTuple* HomMat2D, HTuple* Points1, HTuple* Points2)
HHomMat2D HImage::ProjMatchPointsRansac(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, Hlong RowMove, Hlong ColMove, Hlong RowTolerance, Hlong ColTolerance, const HTuple& Rotation, const HTuple& MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const
HHomMat2D HImage::ProjMatchPointsRansac(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, Hlong RowMove, Hlong ColMove, Hlong RowTolerance, Hlong ColTolerance, double Rotation, Hlong MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const
HHomMat2D HImage::ProjMatchPointsRansac(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const char* GrayMatchMethod, Hlong MaskSize, Hlong RowMove, Hlong ColMove, Hlong RowTolerance, Hlong ColTolerance, double Rotation, Hlong MatchThreshold, const char* EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const
HTuple HHomMat2D::ProjMatchPointsRansac(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, Hlong RowMove, Hlong ColMove, Hlong RowTolerance, Hlong ColTolerance, const HTuple& Rotation, const HTuple& MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points2)
HTuple HHomMat2D::ProjMatchPointsRansac(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, Hlong RowMove, Hlong ColMove, Hlong RowTolerance, Hlong ColTolerance, double Rotation, Hlong MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points2)
HTuple HHomMat2D::ProjMatchPointsRansac(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const char* GrayMatchMethod, Hlong MaskSize, Hlong RowMove, Hlong ColMove, Hlong RowTolerance, Hlong ColTolerance, double Rotation, Hlong MatchThreshold, const char* EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points2)
void HOperatorSetX.ProjMatchPointsRansac(
[in] IHUntypedObjectX* Image1, [in] IHUntypedObjectX* Image2, [in] VARIANT Rows1, [in] VARIANT Cols1, [in] VARIANT Rows2, [in] VARIANT Cols2, [in] VARIANT GrayMatchMethod, [in] VARIANT MaskSize, [in] VARIANT RowMove, [in] VARIANT ColMove, [in] VARIANT RowTolerance, [in] VARIANT ColTolerance, [in] VARIANT Rotation, [in] VARIANT MatchThreshold, [in] VARIANT EstimationMethod, [in] VARIANT DistanceThreshold, [in] VARIANT RandSeed, [out] VARIANT* HomMat2d, [out] VARIANT* Points1, [out] VARIANT* Points2)
IHHomMat2DX* HImageX.ProjMatchPointsRansac(
[in] IHImageX* Image2, [in] VARIANT Rows1, [in] VARIANT Cols1, [in] VARIANT Rows2, [in] VARIANT Cols2, [in] BSTR GrayMatchMethod, [in] Hlong MaskSize, [in] Hlong RowMove, [in] Hlong ColMove, [in] Hlong RowTolerance, [in] Hlong ColTolerance, [in] VARIANT Rotation, [in] VARIANT MatchThreshold, [in] BSTR EstimationMethod, [in] double DistanceThreshold, [in] Hlong RandSeed, [out] VARIANT* Points1, [out] VARIANT* Points2)
VARIANT HHomMat2DX.ProjMatchPointsRansac(
[in] IHImageX* Image1, [in] IHImageX* Image2, [in] VARIANT Rows1, [in] VARIANT Cols1, [in] VARIANT Rows2, [in] VARIANT Cols2, [in] BSTR GrayMatchMethod, [in] Hlong MaskSize, [in] Hlong RowMove, [in] Hlong ColMove, [in] Hlong RowTolerance, [in] Hlong ColTolerance, [in] VARIANT Rotation, [in] VARIANT MatchThreshold, [in] BSTR EstimationMethod, [in] double DistanceThreshold, [in] Hlong RandSeed, [out] VARIANT* Points2)
static void HOperatorSet.ProjMatchPointsRansac(HObject image1, HObject image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, HTuple grayMatchMethod, HTuple maskSize, HTuple rowMove, HTuple colMove, HTuple rowTolerance, HTuple colTolerance, HTuple rotation, HTuple matchThreshold, HTuple estimationMethod, HTuple distanceThreshold, HTuple randSeed, out HTuple homMat2D, out HTuple points1, out HTuple points2)
HHomMat2D HImage.ProjMatchPointsRansac(HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, int rowMove, int colMove, int rowTolerance, int colTolerance, HTuple rotation, HTuple matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out HTuple points1, out HTuple points2)
HHomMat2D HImage.ProjMatchPointsRansac(HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, int rowMove, int colMove, int rowTolerance, int colTolerance, double rotation, int matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out HTuple points1, out HTuple points2)
HTuple HHomMat2D.ProjMatchPointsRansac(HImage image1, HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, int rowMove, int colMove, int rowTolerance, int colTolerance, HTuple rotation, HTuple matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out HTuple points2)
HTuple HHomMat2D.ProjMatchPointsRansac(HImage image1, HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, int rowMove, int colMove, int rowTolerance, int colTolerance, double rotation, int matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out HTuple points2)
Given a set of coordinates of characteristic points
(Cols1Cols1Cols1Cols1Cols1cols1,Rows1Rows1Rows1Rows1Rows1rows1) and
(Cols2Cols2Cols2Cols2Cols2cols2,Rows2Rows2Rows2Rows2Rows2rows2) in both input images
Image1Image1Image1Image1Image1image1 and Image2Image2Image2Image2Image2image2,
proj_match_points_ransacproj_match_points_ransacProjMatchPointsRansacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansac automatically determines
corresponding points and the homogeneous projective transformation
matrix HomMat2DHomMat2DHomMat2DHomMat2DHomMat2DhomMat2D that best transforms the corresponding
points from the different images into each other. The
characteristic points can, for example, be extracted with
points_foerstnerpoints_foerstnerPointsFoerstnerpoints_foerstnerPointsFoerstnerPointsFoerstner or points_harrispoints_harrisPointsHarrispoints_harrisPointsHarrisPointsHarris.
The transformation is determined in two steps: First, gray value
correlations of mask windows around the input points in the first
and the second image are determined and an initial matching between
them is generated using the similarity of the windows in both
images.
The size of the mask windows is MaskSizeMaskSizeMaskSizeMaskSizeMaskSizemaskSize x MaskSizeMaskSizeMaskSizeMaskSizeMaskSizemaskSize. Three
metrics for the correlation can be selected. If
GrayMatchMethodGrayMatchMethodGrayMatchMethodGrayMatchMethodGrayMatchMethodgrayMatchMethod has the value 'ssd'"ssd""ssd""ssd""ssd""ssd", the sum of
the squared gray value differences is used, 'sad'"sad""sad""sad""sad""sad" means the
sum of absolute differences, and 'ncc'"ncc""ncc""ncc""ncc""ncc" is the normalized
cross correlation. For details please refer to
binocular_disparitybinocular_disparityBinocularDisparitybinocular_disparityBinocularDisparityBinocularDisparity. The metric is minimized ('ssd'"ssd""ssd""ssd""ssd""ssd",
'sad'"sad""sad""sad""sad""sad") or maximized ('ncc'"ncc""ncc""ncc""ncc""ncc") over all possible
point pairs. A thus found matching is only accepted if the value of
the metric is below the value of MatchThresholdMatchThresholdMatchThresholdMatchThresholdMatchThresholdmatchThreshold
('ssd'"ssd""ssd""ssd""ssd""ssd", 'sad'"sad""sad""sad""sad""sad") or above that value
('ncc'"ncc""ncc""ncc""ncc""ncc").
To increase the algorithm's performance, the search area for the
matchings can be limited. Only points within a window of 2*RowToleranceRowToleranceRowToleranceRowToleranceRowTolerancerowTolerance x
2*ColToleranceColToleranceColToleranceColToleranceColTolerancecolTolerance points are considered. The offset of the
center of the search window in the second image with respect to the
position of the current point in the first image is given by
RowMoveRowMoveRowMoveRowMoveRowMoverowMove and ColMoveColMoveColMoveColMoveColMovecolMove.
If the transformation contains a rotation, i.e., if the first image
is rotated with respect to the second image, the parameter
RotationRotationRotationRotationRotationrotation may contain an estimate for the rotation angle or
an angle interval in radians. A good guess will increase the quality
of the gray value matching. If the actual rotation differs too much
from the specified estimate the matching will typically fail. The
larger the given interval, the slower the operator is since the
entire algorithm is run for all relevant angles within the interval.
Once the initial matching is complete, a randomized search algorithm
(RANSAC) is used to determine the transformation matrix
HomMat2DHomMat2DHomMat2DHomMat2DHomMat2DhomMat2D. It tries to find the matrix that is consistent
with a maximum number of correspondences. For a point to be
accepted, its distance from the coordinates predicted by the
transformation must not exceed the threshold
DistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholddistanceThreshold.
Once a choice has been made, the matrix is further optimized using
all consistent points. For this optimization, the
EstimationMethodEstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod can be chosen to either be the slow but
mathematically optimal 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard""gold_standard" method or the faster
'normalized_dlt'"normalized_dlt""normalized_dlt""normalized_dlt""normalized_dlt""normalized_dlt". Here, the algorithms of
vector_to_proj_hom_mat2dvector_to_proj_hom_mat2dVectorToProjHomMat2dvector_to_proj_hom_mat2dVectorToProjHomMat2dVectorToProjHomMat2d are used.
Point pairs that still violate the consistency condition for the
final transformation are dropped, the matched points are returned as
control values. Points1Points1Points1Points1Points1points1 contains the indices of the
matched input points from the first image, Points2Points2Points2Points2Points2points2 contains
the indices of the corresponding points in the second image.
The parameter RandSeedRandSeedRandSeedRandSeedRandSeedrandSeed can be used to control the
randomized nature of the RANSAC algorithm, and hence to obtain
reproducible results. If RandSeedRandSeedRandSeedRandSeedRandSeedrandSeed is set to a positive
number, the operator yields the same result on every call with the
same parameters because the internally used random number generator
is initialized with the seed value. If RandSeedRandSeedRandSeedRandSeedRandSeedrandSeed =
0, the random number generator is initialized with the
current time. Hence, the results may not be reproducible in this
case.
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
Row coordinates of characteristic points
in image 1.
Column coordinates of characteristic points
in image 1.
Row coordinates of characteristic points
in image 2.
Column coordinates of characteristic points
in image 2.
Gray value comparison metric.
Default value:
'ssd'
"ssd"
"ssd"
"ssd"
"ssd"
"ssd"
List of values: 'ncc'"ncc""ncc""ncc""ncc""ncc", 'sad'"sad""sad""sad""sad""sad", 'ssd'"ssd""ssd""ssd""ssd""ssd"
Size of gray value masks.
Default value: 10
Typical range of values:
MaskSize
MaskSize
MaskSize
MaskSize
MaskSize
maskSize
≤
90
Average row coordinate shift.
Default value: 0
Average column coordinate shift.
Default value: 0
Half height of matching search window.
Default value: 256
Half width of matching search window.
Default value: 256
Range of rotation angles.
Default value: 0.0
Suggested values: 0.0, 0.7854, 1.571, 3.142
Threshold for gray value matching.
Default value: 10
Suggested values: 10, 20, 50, 100, 0.9, 0.7
Transformation matrix estimation algorithm.
Default value:
'normalized_dlt'
"normalized_dlt"
"normalized_dlt"
"normalized_dlt"
"normalized_dlt"
"normalized_dlt"
List of values: 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard""gold_standard", 'normalized_dlt'"normalized_dlt""normalized_dlt""normalized_dlt""normalized_dlt""normalized_dlt"
Threshold for transformation consistency check.
Default value: 0.2
Seed for the random number generator.
Default value: 0
Homogeneous projective transformation matrix.
Indices of matched input points in image 1.
Indices of matched input points in image 2.
points_foerstnerpoints_foerstnerPointsFoerstnerpoints_foerstnerPointsFoerstnerPointsFoerstner,
points_harrispoints_harrisPointsHarrispoints_harrisPointsHarrisPointsHarris
projective_trans_imageprojective_trans_imageProjectiveTransImageprojective_trans_imageProjectiveTransImageProjectiveTransImage,
projective_trans_image_sizeprojective_trans_image_sizeProjectiveTransImageSizeprojective_trans_image_sizeProjectiveTransImageSizeProjectiveTransImageSize,
projective_trans_regionprojective_trans_regionProjectiveTransRegionprojective_trans_regionProjectiveTransRegionProjectiveTransRegion,
projective_trans_contour_xldprojective_trans_contour_xldProjectiveTransContourXldprojective_trans_contour_xldProjectiveTransContourXldProjectiveTransContourXld,
projective_trans_point_2dprojective_trans_point_2dProjectiveTransPoint2dprojective_trans_point_2dProjectiveTransPoint2dProjectiveTransPoint2d,
projective_trans_pixelprojective_trans_pixelProjectiveTransPixelprojective_trans_pixelProjectiveTransPixelProjectiveTransPixel
hom_vector_to_proj_hom_mat2dhom_vector_to_proj_hom_mat2dHomVectorToProjHomMat2dhom_vector_to_proj_hom_mat2dHomVectorToProjHomMat2dHomVectorToProjHomMat2d,
vector_to_proj_hom_mat2dvector_to_proj_hom_mat2dVectorToProjHomMat2dvector_to_proj_hom_mat2dVectorToProjHomMat2dVectorToProjHomMat2d
proj_match_points_ransac_guidedproj_match_points_ransac_guidedProjMatchPointsRansacGuidedproj_match_points_ransac_guidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuided
Richard Hartley, Andrew Zisserman: “Multiple View Geometry in
Computer Vision”; Cambridge University Press, Cambridge; 2000.
Olivier Faugeras, Quang-Tuan Luong: “The Geometry of Multiple
Images: The Laws That Govern the Formation of Multiple Images of a
Scene and Some of Their Applications”; MIT Press, Cambridge, MA;
2001.
Matching