proj_match_points_distortion_ransacT_proj_match_points_distortion_ransacProjMatchPointsDistortionRansacProjMatchPointsDistortionRansac (Operator)

Name

proj_match_points_distortion_ransacT_proj_match_points_distortion_ransacProjMatchPointsDistortionRansacProjMatchPointsDistortionRansac — Compute a projective transformation matrix between two images and the radial distortion coefficient by automatically finding correspondences between points.

Signature

proj_match_points_distortion_ransac(Image1, Image2 : : Rows1, Cols1, Rows2, Cols2, GrayMatchMethod, MaskSize, RowMove, ColMove, RowTolerance, ColTolerance, Rotation, MatchThreshold, EstimationMethod, DistanceThreshold, RandSeed : HomMat2D, Kappa, Error, Points1, Points2)

Herror T_proj_match_points_distortion_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* Kappa, Htuple* Error, Htuple* Points1, Htuple* Points2)

void ProjMatchPointsDistortionRansac(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* Kappa, HTuple* Error, HTuple* Points1, HTuple* Points2)

HHomMat2D HImage::ProjMatchPointsDistortionRansac(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, const HTuple& DistanceThreshold, Hlong RandSeed, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const

HHomMat2D HImage::ProjMatchPointsDistortionRansac(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, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const

HHomMat2D HImage::ProjMatchPointsDistortionRansac(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, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const

HHomMat2D HImage::ProjMatchPointsDistortionRansac(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const wchar_t* GrayMatchMethod, Hlong MaskSize, Hlong RowMove, Hlong ColMove, Hlong RowTolerance, Hlong ColTolerance, double Rotation, Hlong MatchThreshold, const wchar_t* EstimationMethod, double DistanceThreshold, Hlong RandSeed, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const   (Windows only)

double HHomMat2D::ProjMatchPointsDistortionRansac(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, const HTuple& DistanceThreshold, Hlong RandSeed, double* Error, HTuple* Points1, HTuple* Points2)

double HHomMat2D::ProjMatchPointsDistortionRansac(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, double* Error, HTuple* Points1, HTuple* Points2)

double HHomMat2D::ProjMatchPointsDistortionRansac(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, double* Error, HTuple* Points1, HTuple* Points2)

double HHomMat2D::ProjMatchPointsDistortionRansac(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const wchar_t* GrayMatchMethod, Hlong MaskSize, Hlong RowMove, Hlong ColMove, Hlong RowTolerance, Hlong ColTolerance, double Rotation, Hlong MatchThreshold, const wchar_t* EstimationMethod, double DistanceThreshold, Hlong RandSeed, double* Error, HTuple* Points1, HTuple* Points2)   (Windows only)

static void HOperatorSet.ProjMatchPointsDistortionRansac(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 kappa, out HTuple error, out HTuple points1, out HTuple points2)

HHomMat2D HImage.ProjMatchPointsDistortionRansac(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, HTuple distanceThreshold, int randSeed, out double kappa, out double error, out HTuple points1, out HTuple points2)

HHomMat2D HImage.ProjMatchPointsDistortionRansac(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 double kappa, out double error, out HTuple points1, out HTuple points2)

double HHomMat2D.ProjMatchPointsDistortionRansac(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, HTuple distanceThreshold, int randSeed, out double error, out HTuple points1, out HTuple points2)

double HHomMat2D.ProjMatchPointsDistortionRansac(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 double error, out HTuple points1, out HTuple points2)

Description

Given a set of coordinates of characteristic points (Rows1Rows1Rows1Rows1rows1,Cols1Cols1Cols1Cols1cols1) and (Rows2Rows2Rows2Rows2rows2,Cols2Cols2Cols2Cols2cols2) in both input images Image1Image1Image1Image1image1 and Image2Image2Image2Image2image2, which must be of identical size, proj_match_points_distortion_ransacproj_match_points_distortion_ransacProjMatchPointsDistortionRansacProjMatchPointsDistortionRansacProjMatchPointsDistortionRansac automatically determines corresponding points, the homogeneous projective transformation matrix HomMat2DHomMat2DHomMat2DHomMat2DhomMat2D, and the radial distortion coefficient KappaKappaKappaKappakappa that optimally fulfill the following equation: Here, and denote image points that are obtained by undistorting the input image points with the division model (see Calibration / Multi-View): Here, denote the distorted image points, specified relative to the image center, and w and h denote the width and height of the input images. Thus, proj_match_points_distortion_ransacproj_match_points_distortion_ransacProjMatchPointsDistortionRansacProjMatchPointsDistortionRansacProjMatchPointsDistortionRansac assumes that the principal point of the camera, i.e., the center of the radial distortions, lies at the center of the image.

The returned KappaKappaKappaKappakappa can be used to construct camera parameters that can be used to rectify images or points (see change_radial_distortion_cam_parchange_radial_distortion_cam_parChangeRadialDistortionCamParChangeRadialDistortionCamParChangeRadialDistortionCamPar, change_radial_distortion_imagechange_radial_distortion_imageChangeRadialDistortionImageChangeRadialDistortionImageChangeRadialDistortionImage, and change_radial_distortion_pointschange_radial_distortion_pointsChangeRadialDistortionPointsChangeRadialDistortionPointsChangeRadialDistortionPoints):

The matching process is based on characteristic points, which can be extracted with point operators like points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstner or points_harrispoints_harrisPointsHarrisPointsHarrisPointsHarris. The matching itself is carried out 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. Then, the RANSAC algorithm is applied to find the projective transformation matrix and radial distortion coefficient that maximizes the number of correspondences under the above constraint.

The size of the mask windows used for the matching is MaskSizeMaskSizeMaskSizeMaskSizemaskSize x MaskSizeMaskSizeMaskSizeMaskSizemaskSize. Three metrics for the correlation can be selected. If GrayMatchMethodGrayMatchMethodGrayMatchMethodGrayMatchMethodgrayMatchMethod has the value 'ssd'"ssd""ssd""ssd""ssd", the sum of the squared gray value differences is used, 'sad'"sad""sad""sad""sad" means the sum of absolute differences, and 'ncc'"ncc""ncc""ncc""ncc" is the normalized cross correlation. For details please refer to binocular_disparitybinocular_disparityBinocularDisparityBinocularDisparityBinocularDisparity. The metric is minimized ('ssd'"ssd""ssd""ssd""ssd", 'sad'"sad""sad""sad""sad") or maximized ('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 MatchThresholdMatchThresholdMatchThresholdMatchThresholdmatchThreshold ('ssd'"ssd""ssd""ssd""ssd", 'sad'"sad""sad""sad""sad") or above that value ('ncc'"ncc""ncc""ncc""ncc").

To increase the algorithm's performance, the search area for the match candidates can be limited to a rectangle by specifying its size and offset. Only points within a window of 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 RowMoveRowMoveRowMoveRowMoverowMove and ColMoveColMoveColMoveColMovecolMove.

If the transformation contains a rotation, i.e., if the first image is rotated with respect to the second image, the parameter RotationRotationRotationRotationrotation 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. In this case, an angle interval should be specified and RotationRotationRotationRotationrotation is a tuple with two elements. The larger the given interval is the slower the operator is since the RANSAC algorithm is run over all (automatically determined) angle increments within the interval.

After the initial matching has been completed, a randomized search algorithm (RANSAC) is used to determine the projective transformation matrix HomMat2DHomMat2DHomMat2DHomMat2DhomMat2D and the radial distortion coefficient KappaKappaKappaKappakappa. It tries to find the parameters that are consistent with a maximum number of correspondences. For a point to be accepted, the distance in pixels to its corresponding transformed point must not exceed the threshold DistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholddistanceThreshold.

The parameter EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod determines which algorithm is used to compute the projective transformation matrix. A linear algorithm is used if EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod is set to 'linear'"linear""linear""linear""linear". This algorithm is very fast and returns accurate results for small to moderate noise of the point coordinates and for most distortions (except for small distortions). For EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod = 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard", a mathematically optimal but slower optimization is used, which minimizes the geometric reprojection error. In general, it is preferable to use EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod = 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard".

The value ErrorErrorErrorErrorerror indicates the overall quality of the estimation procedure and is the mean symmetric euclidean distance in pixels between the points and their corresponding transformed points.

Point pairs consistent with the above constraints are considered to be corresponding points. Points1Points1Points1Points1points1 contains the indices of the matched input points from the first image and Points2Points2Points2Points2points2 contains the indices of the corresponding points in the second image.

The parameter RandSeedRandSeedRandSeedRandSeedrandSeed can be used to control the randomized nature of the RANSAC algorithm, and hence to obtain reproducible results. If RandSeedRandSeedRandSeedRandSeedrandSeed is set to a positive number, the operator returns the same result on every call with the same parameters because the internally used random number generator is initialized with RandSeedRandSeedRandSeedRandSeedrandSeed. If RandSeedRandSeedRandSeedRandSeedrandSeed = 0, the random number generator is initialized with the current time. In this case the results may not be reproducible.

Execution Information

Parameters

Image1Image1Image1Image1image1 (input_object)  singlechannelimage objectHImageHImageHobject (byte / uint2)

Input image 1.

Image2Image2Image2Image2image2 (input_object)  singlechannelimage objectHImageHImageHobject (byte / uint2)

Input image 2.

Rows1Rows1Rows1Rows1rows1 (input_control)  point.y-array HTupleHTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Input points in image 1 (row coordinate).

Restriction: length(Rows1) >= 5

Cols1Cols1Cols1Cols1cols1 (input_control)  point.x-array HTupleHTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Input points in image 1 (column coordinate).

Restriction: length(Cols1) == length(Rows1)

Rows2Rows2Rows2Rows2rows2 (input_control)  point.y-array HTupleHTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Input points in image 2 (row coordinate).

Restriction: length(Rows2) >= 5

Cols2Cols2Cols2Cols2cols2 (input_control)  point.x-array HTupleHTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Input points in image 2 (column coordinate).

Restriction: length(Cols2) == length(Rows2)

GrayMatchMethodGrayMatchMethodGrayMatchMethodGrayMatchMethodgrayMatchMethod (input_control)  string HTupleHTupleHtuple (string) (string) (HString) (char*)

Gray value match metric.

Default value: 'ncc' "ncc" "ncc" "ncc" "ncc"

List of values: 'ncc'"ncc""ncc""ncc""ncc", 'sad'"sad""sad""sad""sad", 'ssd'"ssd""ssd""ssd""ssd"

MaskSizeMaskSizeMaskSizeMaskSizemaskSize (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Size of gray value masks.

Default value: 10

Typical range of values: 3 ≤ MaskSize MaskSize MaskSize MaskSize maskSize ≤ 15

Restriction: MaskSize >= 1

RowMoveRowMoveRowMoveRowMoverowMove (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Average row coordinate offset of corresponding points.

Default value: 0

ColMoveColMoveColMoveColMovecolMove (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Average column coordinate offset of corresponding points.

Default value: 0

RowToleranceRowToleranceRowToleranceRowTolerancerowTolerance (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Half height of matching search window.

Default value: 200

Restriction: RowTolerance >= 1

ColToleranceColToleranceColToleranceColTolerancecolTolerance (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Half width of matching search window.

Default value: 200

Restriction: ColTolerance >= 1

RotationRotationRotationRotationrotation (input_control)  angle.rad(-array) HTupleHTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Estimate of the relative rotation of the second image with respect to the first image.

Default value: 0.0

Suggested values: 0.0, 0.1, -0.1, 0.7854, 1.571, 3.142

MatchThresholdMatchThresholdMatchThresholdMatchThresholdmatchThreshold (input_control)  number HTupleHTupleHtuple (integer / real) (int / long / double) (Hlong / double) (Hlong / double)

Threshold for gray value matching.

Default value: 0.7

Suggested values: 0.9, 0.7, 0.5, 10, 20, 50, 100

EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod (input_control)  string HTupleHTupleHtuple (string) (string) (HString) (char*)

Algorithm for the computation of the projective transformation matrix.

Default value: 'gold_standard' "gold_standard" "gold_standard" "gold_standard" "gold_standard"

List of values: 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard", 'linear'"linear""linear""linear""linear"

DistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholddistanceThreshold (input_control)  number HTupleHTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

Threshold for the transformation consistency check.

Default value: 1

Restriction: DistanceThreshold > 0

RandSeedRandSeedRandSeedRandSeedrandSeed (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Seed for the random number generator.

Default value: 0

HomMat2DHomMat2DHomMat2DHomMat2DhomMat2D (output_control)  hom_mat2d HHomMat2D, HTupleHTupleHtuple (real) (double) (double) (double)

Computed homogeneous projective transformation matrix.

KappaKappaKappaKappakappa (output_control)  real HTupleHTupleHtuple (real) (double) (double) (double)

Computed radial distortion coefficient.

ErrorErrorErrorErrorerror (output_control)  real HTupleHTupleHtuple (real) (double) (double) (double)

Root-Mean-Square transformation error.

Points1Points1Points1Points1points1 (output_control)  integer-array HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Indices of matched input points in image 1.

Points2Points2Points2Points2points2 (output_control)  integer-array HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Indices of matched input points in image 2.

Example (HDevelop)

points_foerstner (Image1, 1, 2, 3, 50, 0.1, 'gauss', 'true', \
                  Rows1, Cols1, _, _, _, _, _, _, _, _)
points_foerstner (Image2, 1, 2, 3, 50, 0.1, 'gauss', 'true', \
                  Rows2, Cols2, _, _, _, _, _, _, _, _)
get_image_size (Image1, Width, Height)
proj_match_points_distortion_ransac (Image1, Image2, Rows1, Cols1, \
                                     Rows2, Cols2, 'ncc', 10, 0, 0, \
                                     Height, Width, 0, 0.5, \
                                     'gold_standard', 1, 42, \
                                     HomMat2D, Kappa, Error, \
                                     Points1, Points2)
CamParDist := ['area_scan_division',0.0,Kappa,1.0,1.0, \
               0.5*(Width-1),0.5*(Height-1),Width,Height]
change_radial_distortion_cam_par ('fixed', CamParDist, 0, CamPar)
change_radial_distortion_image (Image1, Image1, Image1Rect, \
                                CamParDist, CamPar)
change_radial_distortion_image (Image2, Image2, Image2Rect, \
                                CamParDist, CamPar)
concat_obj (Image1Rect, Image2Rect, ImagesRect)
gen_projective_mosaic (ImagesRect, MosaicImage, 1, 1, 2, HomMat2D, \
                       'default', 'false', MosaicMatrices2D)

Possible Predecessors

points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstner, points_harrispoints_harrisPointsHarrisPointsHarrisPointsHarris

Possible Successors

vector_to_proj_hom_mat2d_distortionvector_to_proj_hom_mat2d_distortionVectorToProjHomMat2dDistortionVectorToProjHomMat2dDistortionVectorToProjHomMat2dDistortion, change_radial_distortion_cam_parchange_radial_distortion_cam_parChangeRadialDistortionCamParChangeRadialDistortionCamParChangeRadialDistortionCamPar, change_radial_distortion_imagechange_radial_distortion_imageChangeRadialDistortionImageChangeRadialDistortionImageChangeRadialDistortionImage, change_radial_distortion_pointschange_radial_distortion_pointsChangeRadialDistortionPointsChangeRadialDistortionPointsChangeRadialDistortionPoints, gen_binocular_proj_rectificationgen_binocular_proj_rectificationGenBinocularProjRectificationGenBinocularProjRectificationGenBinocularProjRectification, projective_trans_imageprojective_trans_imageProjectiveTransImageProjectiveTransImageProjectiveTransImage, projective_trans_image_sizeprojective_trans_image_sizeProjectiveTransImageSizeProjectiveTransImageSizeProjectiveTransImageSize, projective_trans_regionprojective_trans_regionProjectiveTransRegionProjectiveTransRegionProjectiveTransRegion, projective_trans_contour_xldprojective_trans_contour_xldProjectiveTransContourXldProjectiveTransContourXldProjectiveTransContourXld, projective_trans_point_2dprojective_trans_point_2dProjectiveTransPoint2dProjectiveTransPoint2dProjectiveTransPoint2d, projective_trans_pixelprojective_trans_pixelProjectiveTransPixelProjectiveTransPixelProjectiveTransPixel

Alternatives

proj_match_points_distortion_ransac_guidedproj_match_points_distortion_ransac_guidedProjMatchPointsDistortionRansacGuidedProjMatchPointsDistortionRansacGuidedProjMatchPointsDistortionRansacGuided

See also

proj_match_points_ransacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansacProjMatchPointsRansac, proj_match_points_ransac_guidedproj_match_points_ransac_guidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuided, hom_vector_to_proj_hom_mat2dhom_vector_to_proj_hom_mat2dHomVectorToProjHomMat2dHomVectorToProjHomMat2dHomVectorToProjHomMat2d, vector_to_proj_hom_mat2dvector_to_proj_hom_mat2dVectorToProjHomMat2dVectorToProjHomMat2dVectorToProjHomMat2d

References

Richard Hartley, Andrew Zisserman: “Multiple View Geometry in Computer Vision”; Cambridge University Press, Cambridge; 2003.
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