proj_match_points_ransac_guidedT_proj_match_points_ransac_guidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuidedproj_match_points_ransac_guided (Operator)

Name

proj_match_points_ransac_guidedT_proj_match_points_ransac_guidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuidedproj_match_points_ransac_guided — Compute a projective transformation matrix between two images by finding correspondences between points based on a known approximation of the projective transformation matrix.

Signature

proj_match_points_ransac_guided(Image1, Image2 : : Rows1, Cols1, Rows2, Cols2, GrayMatchMethod, MaskSize, HomMat2DGuide, DistanceTolerance, MatchThreshold, EstimationMethod, DistanceThreshold, RandSeed : HomMat2D, Points1, Points2)

Herror T_proj_match_points_ransac_guided(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 HomMat2DGuide, const Htuple DistanceTolerance, const Htuple MatchThreshold, const Htuple EstimationMethod, const Htuple DistanceThreshold, const Htuple RandSeed, Htuple* HomMat2D, Htuple* Points1, Htuple* Points2)

void ProjMatchPointsRansacGuided(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& HomMat2DGuide, const HTuple& DistanceTolerance, const HTuple& MatchThreshold, const HTuple& EstimationMethod, const HTuple& DistanceThreshold, const HTuple& RandSeed, HTuple* HomMat2D, HTuple* Points1, HTuple* Points2)

HHomMat2D HImage::ProjMatchPointsRansacGuided(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, const HHomMat2D& HomMat2DGuide, double DistanceTolerance, const HTuple& MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const

HHomMat2D HImage::ProjMatchPointsRansacGuided(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, const HHomMat2D& HomMat2DGuide, double DistanceTolerance, Hlong MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const

HHomMat2D HImage::ProjMatchPointsRansacGuided(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const char* GrayMatchMethod, Hlong MaskSize, const HHomMat2D& HomMat2DGuide, double DistanceTolerance, Hlong MatchThreshold, const char* EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const

HHomMat2D HImage::ProjMatchPointsRansacGuided(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const wchar_t* GrayMatchMethod, Hlong MaskSize, const HHomMat2D& HomMat2DGuide, double DistanceTolerance, Hlong MatchThreshold, const wchar_t* EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const   ( Windows only)

HHomMat2D HHomMat2D::ProjMatchPointsRansacGuided(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, double DistanceTolerance, const HTuple& MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const

HHomMat2D HHomMat2D::ProjMatchPointsRansacGuided(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, double DistanceTolerance, Hlong MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const

HHomMat2D HHomMat2D::ProjMatchPointsRansacGuided(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const char* GrayMatchMethod, Hlong MaskSize, double DistanceTolerance, Hlong MatchThreshold, const char* EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const

HHomMat2D HHomMat2D::ProjMatchPointsRansacGuided(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const wchar_t* GrayMatchMethod, Hlong MaskSize, double DistanceTolerance, Hlong MatchThreshold, const wchar_t* EstimationMethod, double DistanceThreshold, Hlong RandSeed, HTuple* Points1, HTuple* Points2) const   ( Windows only)

static void HOperatorSet.ProjMatchPointsRansacGuided(HObject image1, HObject image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, HTuple grayMatchMethod, HTuple maskSize, HTuple homMat2DGuide, HTuple distanceTolerance, HTuple matchThreshold, HTuple estimationMethod, HTuple distanceThreshold, HTuple randSeed, out HTuple homMat2D, out HTuple points1, out HTuple points2)

HHomMat2D HImage.ProjMatchPointsRansacGuided(HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, HHomMat2D homMat2DGuide, double distanceTolerance, HTuple matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out HTuple points1, out HTuple points2)

HHomMat2D HImage.ProjMatchPointsRansacGuided(HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, HHomMat2D homMat2DGuide, double distanceTolerance, int matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out HTuple points1, out HTuple points2)

HHomMat2D HHomMat2D.ProjMatchPointsRansacGuided(HImage image1, HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, double distanceTolerance, HTuple matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out HTuple points1, out HTuple points2)

HHomMat2D HHomMat2D.ProjMatchPointsRansacGuided(HImage image1, HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, double distanceTolerance, int matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out HTuple points1, out HTuple points2)

def proj_match_points_ransac_guided(image_1: HObject, image_2: HObject, rows_1: Sequence[Union[float, int]], cols_1: Sequence[Union[float, int]], rows_2: Sequence[Union[float, int]], cols_2: Sequence[Union[float, int]], gray_match_method: str, mask_size: int, hom_mat_2dguide: Sequence[float], distance_tolerance: float, match_threshold: Union[int, float], estimation_method: str, distance_threshold: float, rand_seed: int) -> Tuple[Sequence[float], Sequence[int], Sequence[int]]

Description

Given a set of coordinates of characteristic points (Cols1Cols1Cols1Cols1cols1cols_1,Rows1Rows1Rows1Rows1rows1rows_1) and (Cols2Cols2Cols2Cols2cols2cols_2,Rows2Rows2Rows2Rows2rows2rows_2) in both input images Image1Image1Image1Image1image1image_1 and Image2Image2Image2Image2image2image_2, and given a known approximation HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_mat_2dguide for the transformation matrix between Image1Image1Image1Image1image1image_1 and Image2Image2Image2Image2image2image_2, proj_match_points_ransac_guidedproj_match_points_ransac_guidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuidedproj_match_points_ransac_guided automatically determines corresponding points and the homogeneous projective transformation matrix HomMat2DHomMat2DHomMat2DHomMat2DhomMat2Dhom_mat_2d that best transforms the corresponding points from the different images into each other. The characteristic points can, for example, be extracted with points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstnerpoints_foerstner or points_harrispoints_harrisPointsHarrisPointsHarrisPointsHarrispoints_harris. The approximation HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_mat_2dguide can, for example, be calculated with proj_match_points_ransacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansacProjMatchPointsRansacproj_match_points_ransac on lower resolution versions of Image1Image1Image1Image1image1image_1 and Image2Image2Image2Image2image2image_2.

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 MaskSizeMaskSizeMaskSizeMaskSizemaskSizemask_size x MaskSizeMaskSizeMaskSizeMaskSizemaskSizemask_size. Three metrics for the correlation can be selected. If GrayMatchMethodGrayMatchMethodGrayMatchMethodGrayMatchMethodgrayMatchMethodgray_match_method 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_disparityBinocularDisparityBinocularDisparityBinocularDisparitybinocular_disparity. 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 MatchThresholdMatchThresholdMatchThresholdMatchThresholdmatchThresholdmatch_threshold ('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 matching operations is limited based on the approximate transformation HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_mat_2dguide. Only points within a distance of DistanceToleranceDistanceToleranceDistanceToleranceDistanceTolerancedistanceTolerancedistance_tolerance around the transformed a point in Image2Image2Image2Image2image2image_2 of a point in Image1Image1Image1Image1image1image_1 via HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_mat_2dguide are considered for the matching.

Once the initial matching is complete, a randomized search algorithm (RANSAC) is used to determine the transformation matrix HomMat2DHomMat2DHomMat2DHomMat2DhomMat2Dhom_mat_2d. 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 DistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholddistanceThresholddistance_threshold.

Once a choice has been made, the matrix is further optimized using all consistent points. For this optimization, the EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method 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_mat2dVectorToProjHomMat2dVectorToProjHomMat2dVectorToProjHomMat2dvector_to_proj_hom_mat2d are used.

Point pairs that still violate the consistency condition for the final transformation are dropped, the matched points are returned as control values. Points1Points1Points1Points1points1points_1 contains the indices of the matched input points from the first image, Points2Points2Points2Points2points2points_2 contains the indices of the corresponding points in the second image.

The parameter RandSeedRandSeedRandSeedRandSeedrandSeedrand_seed can be used to control the randomized nature of the RANSAC algorithm, and hence to obtain reproducible results. If RandSeedRandSeedRandSeedRandSeedrandSeedrand_seed 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 RandSeedRandSeedRandSeedRandSeedrandSeedrand_seed = 0, the random number generator is initialized with the current time. Hence, the results may not be reproducible in this case.

Execution Information

Parameters

Image1Image1Image1Image1image1image_1 (input_object)  singlechannelimage objectHImageHObjectHImageHobject (byte / uint2)

Input image 1.

Image2Image2Image2Image2image2image_2 (input_object)  singlechannelimage objectHImageHObjectHImageHobject (byte / uint2)

Input image 2.

Rows1Rows1Rows1Rows1rows1rows_1 (input_control)  point.x-array HTupleSequence[Union[float, int]]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Row coordinates of characteristic points in image 1.

Cols1Cols1Cols1Cols1cols1cols_1 (input_control)  point.y-array HTupleSequence[Union[float, int]]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Column coordinates of characteristic points in image 1.

Rows2Rows2Rows2Rows2rows2rows_2 (input_control)  point.x-array HTupleSequence[Union[float, int]]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Row coordinates of characteristic points in image 2.

Cols2Cols2Cols2Cols2cols2cols_2 (input_control)  point.y-array HTupleSequence[Union[float, int]]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Column coordinates of characteristic points in image 2.

GrayMatchMethodGrayMatchMethodGrayMatchMethodGrayMatchMethodgrayMatchMethodgray_match_method (input_control)  string HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Gray value comparison metric.

Default: '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"

MaskSizeMaskSizeMaskSizeMaskSizemaskSizemask_size (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Size of gray value masks.

Default: 10

Value range: MaskSize MaskSize MaskSize MaskSize maskSize mask_size ≤ 90

HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_mat_2dguide (input_control)  hom_mat2d HHomMat2D, HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Approximation of the Homogeneous projective transformation matrix between the two images.

DistanceToleranceDistanceToleranceDistanceToleranceDistanceTolerancedistanceTolerancedistance_tolerance (input_control)  real HTuplefloatHTupleHtuple (real) (double) (double) (double)

Tolerance for the matching search window.

Default: 20.0

Suggested values: 0.2, 0.5, 1.0, 2.0, 3.0, 5.0, 10.0, 20.0, 50.0

MatchThresholdMatchThresholdMatchThresholdMatchThresholdmatchThresholdmatch_threshold (input_control)  number HTupleUnion[int, float]HTupleHtuple (integer / real) (int / long / double) (Hlong / double) (Hlong / double)

Threshold for gray value matching.

Default: 10

Suggested values: 10, 20, 50, 100, 0.9, 0.7

EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method (input_control)  string HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Transformation matrix estimation algorithm.

Default: '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"

DistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholddistanceThresholddistance_threshold (input_control)  real HTuplefloatHTupleHtuple (real) (double) (double) (double)

Threshold for transformation consistency check.

Default: 0.2

RandSeedRandSeedRandSeedRandSeedrandSeedrand_seed (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Seed for the random number generator.

Default: 0

HomMat2DHomMat2DHomMat2DHomMat2DhomMat2Dhom_mat_2d (output_control)  hom_mat2d HHomMat2D, HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Homogeneous projective transformation matrix.

Points1Points1Points1Points1points1points_1 (output_control)  integer-array HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Indices of matched input points in image 1.

Points2Points2Points2Points2points2points_2 (output_control)  integer-array HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Indices of matched input points in image 2.

Example (HDevelop)

zoom_image_factor (Image1, Image1Zoomed, 0.5, 0.5, 'constant')
zoom_image_factor (Image2, Image2Zoomed, 0.5, 0.5, 'constant')
points_foerstner (Image1Zoomed, 1, 2, 3, 200, 0.3, 'gauss', 'false', \
                  Rows1, Cols1, _, _, _, _, _, _, _, _)
points_foerstner (Image2Zoomed, 1, 2, 3, 200, 0.3, 'gauss', 'false', \
                  Rows2, Cols2, _, _, _, _, _, _, _, _)
get_image_pointer1 (Image1Zoomed, Pointer, Type, Width, Height)
proj_match_points_ransac (Image1Zoomed, Image2Zoomed, Rows1, Cols1, \
                          Rows2, Cols2, 'ncc', 10, 0, 0, \
                          Height, Width, 0, 0.5, 'gold_standard', \
                          5, 0, HomMat2D, Points1, Points2)
hom_mat2d_scale_local (HomMat2D, 0.5, 0.5, HomMat2DGuide)
hom_mat2d_scale (HomMat2DGuide, 2, 2, 0, 0, HomMat2DGuide)
points_foerstner (Image1, 1, 2, 3, 200, 0.3, 'gauss', 'false', \
                  Rows1, Cols1, _, _, _, _, _, _, _, _)
points_foerstner (Image2, 1, 2, 3, 200, 0.3, 'gauss', 'false', \
                  Rows2, Cols2, _, _, _, _, _, _, _, _)
proj_match_points_ransac_guided (Image1, Image2, Rows1, Cols1, \
                                 Rows2, Cols2, 'ncc', 10, \
                                 HomMat2DGuide, 40, 0.5, \
                                 'gold_standard', 10, 0, HomMat2D, \
                                 Points1, Points2)

Possible Predecessors

points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstnerpoints_foerstner, points_harrispoints_harrisPointsHarrisPointsHarrisPointsHarrispoints_harris

Possible Successors

projective_trans_imageprojective_trans_imageProjectiveTransImageProjectiveTransImageProjectiveTransImageprojective_trans_image, projective_trans_image_sizeprojective_trans_image_sizeProjectiveTransImageSizeProjectiveTransImageSizeProjectiveTransImageSizeprojective_trans_image_size, projective_trans_regionprojective_trans_regionProjectiveTransRegionProjectiveTransRegionProjectiveTransRegionprojective_trans_region, projective_trans_contour_xldprojective_trans_contour_xldProjectiveTransContourXldProjectiveTransContourXldProjectiveTransContourXldprojective_trans_contour_xld, projective_trans_point_2dprojective_trans_point_2dProjectiveTransPoint2dProjectiveTransPoint2dProjectiveTransPoint2dprojective_trans_point_2d, projective_trans_pixelprojective_trans_pixelProjectiveTransPixelProjectiveTransPixelProjectiveTransPixelprojective_trans_pixel

Alternatives

hom_vector_to_proj_hom_mat2dhom_vector_to_proj_hom_mat2dHomVectorToProjHomMat2dHomVectorToProjHomMat2dHomVectorToProjHomMat2dhom_vector_to_proj_hom_mat2d, vector_to_proj_hom_mat2dvector_to_proj_hom_mat2dVectorToProjHomMat2dVectorToProjHomMat2dVectorToProjHomMat2dvector_to_proj_hom_mat2d

See also

proj_match_points_ransacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansacProjMatchPointsRansacproj_match_points_ransac

References

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.

Module

Matching