match_fundamental_matrix_distortion_ransacT_match_fundamental_matrix_distortion_ransacMatchFundamentalMatrixDistortionRansacMatchFundamentalMatrixDistortionRansacmatch_fundamental_matrix_distortion_ransac (Operator)

Name

match_fundamental_matrix_distortion_ransacT_match_fundamental_matrix_distortion_ransacMatchFundamentalMatrixDistortionRansacMatchFundamentalMatrixDistortionRansacmatch_fundamental_matrix_distortion_ransac — Compute the fundamental matrix and the radial distortion coefficient for a pair of stereo images by automatically finding correspondences between image points.

Signature

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

Herror T_match_fundamental_matrix_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* FMatrix, Htuple* Kappa, Htuple* Error, Htuple* Points1, Htuple* Points2)

void MatchFundamentalMatrixDistortionRansac(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* FMatrix, HTuple* Kappa, HTuple* Error, HTuple* Points1, HTuple* Points2)

HHomMat2D HImage::MatchFundamentalMatrixDistortionRansac(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::MatchFundamentalMatrixDistortionRansac(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::MatchFundamentalMatrixDistortionRansac(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::MatchFundamentalMatrixDistortionRansac(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::MatchFundamentalMatrixDistortionRansac(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::MatchFundamentalMatrixDistortionRansac(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::MatchFundamentalMatrixDistortionRansac(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::MatchFundamentalMatrixDistortionRansac(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.MatchFundamentalMatrixDistortionRansac(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 FMatrix, out HTuple kappa, out HTuple error, out HTuple points1, out HTuple points2)

HHomMat2D HImage.MatchFundamentalMatrixDistortionRansac(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.MatchFundamentalMatrixDistortionRansac(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.MatchFundamentalMatrixDistortionRansac(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.MatchFundamentalMatrixDistortionRansac(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)

def match_fundamental_matrix_distortion_ransac(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, row_move: int, col_move: int, row_tolerance: int, col_tolerance: int, rotation: MaybeSequence[Union[float, int]], match_threshold: Union[int, float], estimation_method: str, distance_threshold: Union[float, int], rand_seed: int) -> Tuple[Sequence[float], float, float, Sequence[int], Sequence[int]]

Description

Given a set of coordinates of characteristic points (Rows1Rows1Rows1Rows1rows1rows_1,Cols1Cols1Cols1Cols1cols1cols_1) and (Rows2Rows2Rows2Rows2rows2rows_2,Cols2Cols2Cols2Cols2cols2cols_2) in the stereo images Image1Image1Image1Image1image1image_1 and Image2Image2Image2Image2image2image_2, which must be of identical size, match_fundamental_matrix_distortion_ransacmatch_fundamental_matrix_distortion_ransacMatchFundamentalMatrixDistortionRansacMatchFundamentalMatrixDistortionRansacMatchFundamentalMatrixDistortionRansacmatch_fundamental_matrix_distortion_ransac automatically finds the correspondences between the characteristic points and determines the geometry of the stereo setup. For unknown cameras the geometry of the stereo setup is represented by the fundamental matrix FMatrixFMatrixFMatrixFMatrixFMatrixfmatrix and the radial distortion coefficient KappaKappaKappaKappakappakappa . All corresponding points must fulfill the epipolar constraint: Here, denote image points that are obtained by undistorting the input image points with the division model (see Calibration): 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, match_fundamental_matrix_distortion_ransacmatch_fundamental_matrix_distortion_ransacMatchFundamentalMatrixDistortionRansacMatchFundamentalMatrixDistortionRansacMatchFundamentalMatrixDistortionRansacmatch_fundamental_matrix_distortion_ransac 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 KappaKappaKappaKappakappakappa 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_parChangeRadialDistortionCamParChangeRadialDistortionCamParChangeRadialDistortionCamParchange_radial_distortion_cam_par, change_radial_distortion_imagechange_radial_distortion_imageChangeRadialDistortionImageChangeRadialDistortionImageChangeRadialDistortionImagechange_radial_distortion_image, and change_radial_distortion_pointschange_radial_distortion_pointsChangeRadialDistortionPointsChangeRadialDistortionPointsChangeRadialDistortionPointschange_radial_distortion_points):

Note the column/row ordering in the point coordinates above: since the fundamental matrix encodes the projective relation between two stereo images embedded in 3D space, the x/y notation must be compliant with the camera coordinate system. Therefore, (x,y) coordinates correspond to (column,row) pairs.

The matching process is based on characteristic points, which can be extracted with point operators like points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstnerpoints_foerstner or points_harrispoints_harrisPointsHarrisPointsHarrisPointsHarrispoints_harris. 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 fundamental matrix and radial distortion coefficient that maximizes the number of correspondences under the epipolar constraint.

The size of the mask windows used for the matching 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 matching thus found 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 speed of the algorithm 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 RowMoveRowMoveRowMoveRowMoverowMoverow_move and ColMoveColMoveColMoveColMovecolMovecol_move.

If the second camera is rotated around the optical axis with respect to the first camera, 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. In this case, an angle interval should be specified and RotationRotationRotationRotationrotationrotation is a tuple with two elements. The larger the given interval is the slower is 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 fundamental matrix FMatrixFMatrixFMatrixFMatrixFMatrixfmatrix and the radial distortion coefficient KappaKappaKappaKappakappakappa. 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 epipolar line must not exceed the threshold DistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholddistanceThresholddistance_threshold.

The parameter EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method decides whether the relative orientation between the cameras is of a special type and which algorithm is to be applied for its computation. If EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method is either 'linear'"linear""linear""linear""linear""linear" or 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard""gold_standard", the relative orientation is arbitrary. If the left and right cameras are identical and the relative orientation between them is a pure translation, EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method can be set to 'trans_linear'"trans_linear""trans_linear""trans_linear""trans_linear""trans_linear" or 'trans_gold_standard'"trans_gold_standard""trans_gold_standard""trans_gold_standard""trans_gold_standard""trans_gold_standard". The typical application for this special motion case is the scenario of a single fixed camera looking onto a moving conveyor belt. In order to get a unique solution for the correspondence problem, the minimum required number of corresponding points is nine in the general case and four in the special translational case.

The fundamental matrix is computed by a linear algorithm if EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method is set to 'linear'"linear""linear""linear""linear""linear" or 'trans_linear'"trans_linear""trans_linear""trans_linear""trans_linear""trans_linear". This algorithm is very fast. For the pure translation case (EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method = 'trans_linear'"trans_linear""trans_linear""trans_linear""trans_linear""trans_linear"), the linear method returns accurate results for small to moderate noise of the point coordinates and for most distortions (except for very small distortions). For a general relative orientation of the two cameras (EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method = 'linear'"linear""linear""linear""linear""linear"), the linear method only returns accurate results for very small noise of the point coordinates and for sufficiently large distortions. For EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method = 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard""gold_standard" or 'trans_gold_standard'"trans_gold_standard""trans_gold_standard""trans_gold_standard""trans_gold_standard""trans_gold_standard", a mathematically optimal but slower optimization is used, which minimizes the geometric reprojection error of reconstructed projective 3D points. For a general relative orientation of the two cameras, in general EstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method = 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard""gold_standard" should be selected.

The value ErrorErrorErrorErrorerrorerror indicates the overall quality of the estimation procedure and is the mean symmetric Euclidian distance in pixels between the points and their corresponding epipolar lines.

Point pairs consistent with the above constraints are considered to be corresponding points. Points1Points1Points1Points1points1points_1 contains the indices of the matched input points from the first image and 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 returns the same result on every call with the same parameters because the internally used random number generator is initialized with RandSeedRandSeedRandSeedRandSeedrandSeedrand_seed. If RandSeedRandSeedRandSeedRandSeedrandSeedrand_seed = 0, the random number generator is initialized with the current time. In this case the results may not be reproducible.

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

Input points in image 1 (row coordinate).

Restriction: length(Rows1) >= 9 || length(Rows1) >= 4

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

Input points in image 1 (column coordinate).

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

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

Input points in image 2 (row coordinate).

Restriction: length(Rows2) >= 9 || length(Rows2) >= 4

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

Input points in image 2 (column coordinate).

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

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

Gray value match metric.

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

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 value: 10

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

Restriction: MaskSize >= 1

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

Average row coordinate offset of corresponding points.

Default value: 0

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

Average column coordinate offset of corresponding points.

Default value: 0

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

Half height of matching search window.

Default value: 200

Restriction: RowTolerance >= 1

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

Half width of matching search window.

Default value: 200

Restriction: ColTolerance >= 1

RotationRotationRotationRotationrotationrotation (input_control)  angle.rad(-array) HTupleMaybeSequence[Union[float, int]]HTupleHtuple (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

MatchThresholdMatchThresholdMatchThresholdMatchThresholdmatchThresholdmatch_threshold (input_control)  number HTupleUnion[int, float]HTupleHtuple (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

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

Algorithm for the computation of the fundamental matrix and for special camera orientations.

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

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

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

Maximal deviation of a point from its epipolar line.

Default value: 1

Restriction: DistanceThreshold > 0

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

Seed for the random number generator.

Default value: 0

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

Computed fundamental matrix.

KappaKappaKappaKappakappakappa (output_control)  real HTuplefloatHTupleHtuple (real) (double) (double) (double)

Computed radial distortion coefficient.

ErrorErrorErrorErrorerrorerror (output_control)  real HTuplefloatHTupleHtuple (real) (double) (double) (double)

Root-Mean-Square epipolar distance error.

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)

points_foerstner (Image1, 1, 2, 3, 200, 0.1, 'gauss', 'true', \
                  Rows1, Cols1, _, _, _, _, _, _, _, _)
points_foerstner (Image2, 1, 2, 3, 200, 0.1, 'gauss', 'true', \
                  Rows2, Cols2, _, _, _, _, _, _, _, _)
match_fundamental_matrix_distortion_ransac (Image1, Image2, \
                                            Rows1, Cols1, Rows2, \
                                            Cols2, 'ncc', 10, 0, 0, \
                                            100, 200, 0, 0.5, \
                                            'trans_gold_standard', \
                                            1, 42, FMatrix, Kappa, \
                                            Error, Points1, Points2)
get_image_size (Image1, Width, Height)
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)
gen_binocular_proj_rectification (Map1, Map2, FMatrix, [], Width, \
                                  Height, Width, Height, 1, \
                                  'bilinear_map', _, H1, H2)
map_image (Image1Rect, Map1, Image1Mapped)
map_image (Image2Rect, Map2, Image2Mapped)
binocular_disparity_mg (Image1Mapped, Image2Mapped, Disparity, \
                        Score, 1, 30, 8, 0, 'false', \
                        'default_parameters', 'fast_accurate')

Possible Predecessors

points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstnerpoints_foerstner, points_harrispoints_harrisPointsHarrisPointsHarrisPointsHarrispoints_harris

Possible Successors

vector_to_fundamental_matrix_distortionvector_to_fundamental_matrix_distortionVectorToFundamentalMatrixDistortionVectorToFundamentalMatrixDistortionVectorToFundamentalMatrixDistortionvector_to_fundamental_matrix_distortion, change_radial_distortion_cam_parchange_radial_distortion_cam_parChangeRadialDistortionCamParChangeRadialDistortionCamParChangeRadialDistortionCamParchange_radial_distortion_cam_par, change_radial_distortion_imagechange_radial_distortion_imageChangeRadialDistortionImageChangeRadialDistortionImageChangeRadialDistortionImagechange_radial_distortion_image, change_radial_distortion_pointschange_radial_distortion_pointsChangeRadialDistortionPointsChangeRadialDistortionPointsChangeRadialDistortionPointschange_radial_distortion_points, gen_binocular_proj_rectificationgen_binocular_proj_rectificationGenBinocularProjRectificationGenBinocularProjRectificationGenBinocularProjRectificationgen_binocular_proj_rectification

See also

match_fundamental_matrix_ransacmatch_fundamental_matrix_ransacMatchFundamentalMatrixRansacMatchFundamentalMatrixRansacMatchFundamentalMatrixRansacmatch_fundamental_matrix_ransac, match_essential_matrix_ransacmatch_essential_matrix_ransacMatchEssentialMatrixRansacMatchEssentialMatrixRansacMatchEssentialMatrixRansacmatch_essential_matrix_ransac, match_rel_pose_ransacmatch_rel_pose_ransacMatchRelPoseRansacMatchRelPoseRansacMatchRelPoseRansacmatch_rel_pose_ransac, proj_match_points_ransacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansacProjMatchPointsRansacproj_match_points_ransac, calibrate_camerascalibrate_camerasCalibrateCamerasCalibrateCamerasCalibrateCamerascalibrate_cameras

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

3D Metrology