Name
proj_match_points_distortion_ransac_guidedT_proj_match_points_distortion_ransac_guidedProjMatchPointsDistortionRansacGuidedproj_match_points_distortion_ransac_guidedProjMatchPointsDistortionRansacGuidedProjMatchPointsDistortionRansacGuided — Compute a projective transformation matrix and the radial distortion
coefficient between two images by finding correspondences between
points based on known approximations of the projective
transformation matrix and the radial distortion coefficient.
proj_match_points_distortion_ransac_guided(Image1, Image2 : : Rows1, Cols1, Rows2, Cols2, GrayMatchMethod, MaskSize, HomMat2DGuide, KappaGuide, DistanceTolerance, MatchThreshold, EstimationMethod, DistanceThreshold, RandSeed : HomMat2D, Kappa, Error, Points1, Points2)
Herror T_proj_match_points_distortion_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 KappaGuide, const Htuple DistanceTolerance, const Htuple MatchThreshold, const Htuple EstimationMethod, const Htuple DistanceThreshold, const Htuple RandSeed, Htuple* HomMat2D, Htuple* Kappa, Htuple* Error, Htuple* Points1, Htuple* Points2)
Herror proj_match_points_distortion_ransac_guided(Hobject Image1, 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& KappaGuide, const HTuple& DistanceTolerance, const HTuple& MatchThreshold, const HTuple& EstimationMethod, const HTuple& DistanceThreshold, const HTuple& RandSeed, HTuple* HomMat2D, HTuple* Kappa, HTuple* Error, HTuple* Points1, HTuple* Points2)
HTuple HImage::ProjMatchPointsDistortionRansacGuided(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HTuple& GrayMatchMethod, const HTuple& MaskSize, const HTuple& HomMat2DGuide, const HTuple& KappaGuide, const HTuple& DistanceTolerance, const HTuple& MatchThreshold, const HTuple& EstimationMethod, const HTuple& DistanceThreshold, const HTuple& RandSeed, HTuple* Kappa, HTuple* Error, HTuple* Points1, HTuple* Points2) const
void ProjMatchPointsDistortionRansacGuided(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& KappaGuide, const HTuple& DistanceTolerance, const HTuple& MatchThreshold, const HTuple& EstimationMethod, const HTuple& DistanceThreshold, const HTuple& RandSeed, HTuple* HomMat2D, HTuple* Kappa, HTuple* Error, HTuple* Points1, HTuple* Points2)
HHomMat2D HImage::ProjMatchPointsDistortionRansacGuided(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, const HHomMat2D& HomMat2DGuide, double KappaGuide, double DistanceTolerance, const HTuple& MatchThreshold, const HString& EstimationMethod, const HTuple& DistanceThreshold, Hlong RandSeed, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const
HHomMat2D HImage::ProjMatchPointsDistortionRansacGuided(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, const HHomMat2D& HomMat2DGuide, double KappaGuide, double DistanceTolerance, Hlong MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const
HHomMat2D HImage::ProjMatchPointsDistortionRansacGuided(const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const char* GrayMatchMethod, Hlong MaskSize, const HHomMat2D& HomMat2DGuide, double KappaGuide, double DistanceTolerance, Hlong MatchThreshold, const char* EstimationMethod, double DistanceThreshold, Hlong RandSeed, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const
HHomMat2D HHomMat2D::ProjMatchPointsDistortionRansacGuided(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, double KappaGuide, double DistanceTolerance, const HTuple& MatchThreshold, const HString& EstimationMethod, const HTuple& DistanceThreshold, Hlong RandSeed, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const
HHomMat2D HHomMat2D::ProjMatchPointsDistortionRansacGuided(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const HString& GrayMatchMethod, Hlong MaskSize, double KappaGuide, double DistanceTolerance, Hlong MatchThreshold, const HString& EstimationMethod, double DistanceThreshold, Hlong RandSeed, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const
HHomMat2D HHomMat2D::ProjMatchPointsDistortionRansacGuided(const HImage& Image1, const HImage& Image2, const HTuple& Rows1, const HTuple& Cols1, const HTuple& Rows2, const HTuple& Cols2, const char* GrayMatchMethod, Hlong MaskSize, double KappaGuide, double DistanceTolerance, Hlong MatchThreshold, const char* EstimationMethod, double DistanceThreshold, Hlong RandSeed, double* Kappa, double* Error, HTuple* Points1, HTuple* Points2) const
void HOperatorSetX.ProjMatchPointsDistortionRansacGuided(
[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 HomMat2dGuide, [in] VARIANT KappaGuide, [in] VARIANT DistanceTolerance, [in] VARIANT MatchThreshold, [in] VARIANT EstimationMethod, [in] VARIANT DistanceThreshold, [in] VARIANT RandSeed, [out] VARIANT* HomMat2d, [out] VARIANT* Kappa, [out] VARIANT* Error, [out] VARIANT* Points1, [out] VARIANT* Points2)
IHHomMat2DX* HImageX.ProjMatchPointsDistortionRansacGuided(
[in] IHImageX* Image2, [in] VARIANT Rows1, [in] VARIANT Cols1, [in] VARIANT Rows2, [in] VARIANT Cols2, [in] BSTR GrayMatchMethod, [in] Hlong MaskSize, [in] IHHomMat2DX* HomMat2dGuide, [in] double KappaGuide, [in] double DistanceTolerance, [in] VARIANT MatchThreshold, [in] BSTR EstimationMethod, [in] VARIANT DistanceThreshold, [in] Hlong RandSeed, [out] double* Kappa, [out] double* Error, [out] VARIANT* Points1, [out] VARIANT* Points2)
IHHomMat2DX* HHomMat2DX.ProjMatchPointsDistortionRansacGuided(
[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] double KappaGuide, [in] double DistanceTolerance, [in] VARIANT MatchThreshold, [in] BSTR EstimationMethod, [in] VARIANT DistanceThreshold, [in] Hlong RandSeed, [out] double* Kappa, [out] double* Error, [out] VARIANT* Points1, [out] VARIANT* Points2)
static void HOperatorSet.ProjMatchPointsDistortionRansacGuided(HObject image1, HObject image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, HTuple grayMatchMethod, HTuple maskSize, HTuple homMat2DGuide, HTuple kappaGuide, HTuple distanceTolerance, 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.ProjMatchPointsDistortionRansacGuided(HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, HHomMat2D homMat2DGuide, double kappaGuide, double distanceTolerance, HTuple matchThreshold, string estimationMethod, HTuple distanceThreshold, int randSeed, out double kappa, out double error, out HTuple points1, out HTuple points2)
HHomMat2D HImage.ProjMatchPointsDistortionRansacGuided(HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, HHomMat2D homMat2DGuide, double kappaGuide, double distanceTolerance, int matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out double kappa, out double error, out HTuple points1, out HTuple points2)
HHomMat2D HHomMat2D.ProjMatchPointsDistortionRansacGuided(HImage image1, HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, double kappaGuide, double distanceTolerance, HTuple matchThreshold, string estimationMethod, HTuple distanceThreshold, int randSeed, out double kappa, out double error, out HTuple points1, out HTuple points2)
HHomMat2D HHomMat2D.ProjMatchPointsDistortionRansacGuided(HImage image1, HImage image2, HTuple rows1, HTuple cols1, HTuple rows2, HTuple cols2, string grayMatchMethod, int maskSize, double kappaGuide, double distanceTolerance, int matchThreshold, string estimationMethod, double distanceThreshold, int randSeed, out double kappa, out double error, out HTuple points1, out HTuple points2)
Given a set of coordinates of characteristic points
(Rows1Rows1Rows1Rows1Rows1rows1,Cols1Cols1Cols1Cols1Cols1cols1) and
(Rows2Rows2Rows2Rows2Rows2rows2Cols2Cols2Cols2Cols2Cols2cols2) in both input images
Image1Image1Image1Image1Image1image1 and Image2Image2Image2Image2Image2image2, which must have identical size,
and given known approximations HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuide and
KappaGuideKappaGuideKappaGuideKappaGuideKappaGuidekappaGuide for the transformation matrix and the radial
distortion coefficient between Image1Image1Image1Image1Image1image1 and Image2Image2Image2Image2Image2image2,
proj_match_points_distortion_ransac_guidedproj_match_points_distortion_ransac_guidedProjMatchPointsDistortionRansacGuidedproj_match_points_distortion_ransac_guidedProjMatchPointsDistortionRansacGuidedProjMatchPointsDistortionRansacGuided automatically
determines corresponding points, the homogeneous projective
transformation matrix HomMat2DHomMat2DHomMat2DHomMat2DHomMat2DhomMat2D, and the radial distortion
coefficient KappaKappaKappaKappaKappakappa that optimally fulfill the
following equation:
/ r2 \ / r1 \
| c2 | = HomMat2D * | c1 |.
\ 1 / \ 1 /
Here, (r1,c1) and (r2,c2)
denote image points that are obtained by undistorting the input
image points with the division model (see
calibrate_camerascalibrate_camerasCalibrateCamerascalibrate_camerasCalibrateCamerasCalibrateCameras):
r = r' / (1+Kappa*(r'^2+c'^2)
c = c' / (1+Kappa*(r'^2+c'^2)
Here, (r1',c1') =
(Rows1Rows1Rows1Rows1Rows1rows1-0.5*(h-1),Cols1Cols1Cols1Cols1Cols1cols1-0.5*(w-1)) and (r2',c2') =
(Rows2Rows2Rows2Rows2Rows2rows2-0.5*(h-1),Cols2Cols2Cols2Cols2Cols2cols2-0.5*(w-1)) 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_ransac_guidedproj_match_points_distortion_ransac_guidedProjMatchPointsDistortionRansacGuidedproj_match_points_distortion_ransac_guidedProjMatchPointsDistortionRansacGuidedProjMatchPointsDistortionRansacGuided 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_parChangeRadialDistortionCamParchange_radial_distortion_cam_parChangeRadialDistortionCamParChangeRadialDistortionCamPar,
change_radial_distortion_imagechange_radial_distortion_imageChangeRadialDistortionImagechange_radial_distortion_imageChangeRadialDistortionImageChangeRadialDistortionImage, and
change_radial_distortion_pointschange_radial_distortion_pointsChangeRadialDistortionPointschange_radial_distortion_pointsChangeRadialDistortionPointsChangeRadialDistortionPoints):
CamPar = [0.0,KappaKappaKappaKappaKappakappa,1.0,1.0,0.5*(w-1),0.5*(h-1),w,h]
The approximations HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuide and KappaGuideKappaGuideKappaGuideKappaGuideKappaGuidekappaGuide
can, for example, be calculated with
proj_match_points_distortion_ransacproj_match_points_distortion_ransacProjMatchPointsDistortionRansacproj_match_points_distortion_ransacProjMatchPointsDistortionRansacProjMatchPointsDistortionRansac on lower resolution
versions of Image1Image1Image1Image1Image1image1 and Image2Image2Image2Image2Image2image2. See the example
below.
The matching process is based on characteristic points, which can be
extracted with point operators like points_foerstnerpoints_foerstnerPointsFoerstnerpoints_foerstnerPointsFoerstnerPointsFoerstner or
points_harrispoints_harrisPointsHarrispoints_harrisPointsHarrisPointsHarris. 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
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
match candidates is limited based on the approximate transformation
specified by HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuide and KappaGuideKappaGuideKappaGuideKappaGuideKappaGuidekappaGuide. Only
points within a distance of DistanceToleranceDistanceToleranceDistanceToleranceDistanceToleranceDistanceTolerancedistanceTolerance around the
point in Image2Image2Image2Image2Image2image2 that is obtained when transforming a point
in Image1Image1Image1Image1Image1image1 via HomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuide and
KappaGuideKappaGuideKappaGuideKappaGuideKappaGuidekappaGuide are considered for the matching.
After the initial matching has been completed, a randomized search
algorithm (RANSAC) is used to determine the projective
transformation matrix HomMat2DHomMat2DHomMat2DHomMat2DHomMat2DhomMat2D 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 to its corresponding transformed
point must not exceed the threshold DistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholddistanceThreshold.
Consequently, DistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholdDistanceThresholddistanceThreshold should be smaller than
DistanceToleranceDistanceToleranceDistanceToleranceDistanceToleranceDistanceTolerancedistanceTolerance.
The parameter EstimationMethodEstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod determines which algorithm
is used to compute the projective transformation matrix. A linear
algorithm is used if EstimationMethodEstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod is set to
'linear'"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
EstimationMethodEstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod = 'gold_standard'"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 EstimationMethodEstimationMethodEstimationMethodEstimationMethodEstimationMethodestimationMethod =
'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard""gold_standard".
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 transformed
points.
Point pairs consistent with the above constraints are considered to
be corresponding points. Points1Points1Points1Points1Points1points1 contains the indices of
the matched input points from the first image and 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 returns the same result on every call with the
same parameters because the internally used random number generator
is initialized with RandSeedRandSeedRandSeedRandSeedRandSeedrandSeed. If RandSeedRandSeedRandSeedRandSeedRandSeedrandSeed =
0, the random number generator is initialized with the
current time. In this case the results may not be reproducible.
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
Input points in image 1 (row coordinate).
Restriction: length(Rows1) >= 5
Input points in image 1 (column coordinate).
Restriction: length(Cols1) == length(Rows1)
Input points in image 2 (row coordinate).
Restriction: length(Rows2) >= 5
Input points in image 2 (column coordinate).
Restriction: length(Cols2) == length(Rows2)
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"
Size of gray value masks.
Default value: 10
Typical range of values: 3
≤
MaskSize
MaskSize
MaskSize
MaskSize
MaskSize
maskSize
≤
15
Restriction: MaskSize >= 1
Approximation of the homogeneous projective
transformation matrix between the two images.
Approximation of the radial distortion coefficient
in the two images.
Tolerance for the matching search window.
Default value: 20.0
Suggested values: 0.2, 0.5, 1.0, 2.0, 3.0, 5.0, 10.0, 20.0, 50.0
Restriction: DistanceTolerance > 0
Threshold for gray value matching.
Default value: 0.7
Suggested values: 0.9, 0.7, 0.5, 10, 20, 50, 100
Algorithm for the computation of the projective
transformation matrix.
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"
Threshold for transformation consistency check.
Default value: 1
Restriction: DistanceThreshold > 0
Seed for the random number generator.
Default value: 0
Computed homogeneous projective transformation matrix.
Computed radial distortion coefficient.
Root-Mean-Square transformation error.
Indices of matched input points in image 1.
Indices of matched input points in image 2.
Factor := 0.5
zoom_image_factor (Image1, Image1Zoomed, Factor, Factor, 'constant')
zoom_image_factor (Image2, Image2Zoomed, Factor, Factor, 'constant')
points_foerstner (Image1Zoomed, 1, 2, 3, 200, 0.3, 'gauss', 'true', \
Rows1, Cols1, _, _, _, _, _, _, _, _)
points_foerstner (Image2Zoomed, 1, 2, 3, 200, 0.3, 'gauss', 'true', \
Rows2, Cols2, _, _, _, _, _, _, _, _)
get_image_size (Image1Zoomed, Width, Height)
proj_match_points_distortion_ransac (Image1Zoomed, Image2Zoomed, \
Rows1, Cols1, Rows2, Cols2, \
'ncc', 10, 0, 0, Height, Width, \
0, 0.5, 'gold_standard', 2, 0, \
HomMat2D, Kappa, Error, \
Points1, Points2)
hom_mat2d_scale_local (HomMat2D, Factor, Factor, HomMat2DGuide)
hom_mat2d_scale (HomMat2DGuide, 1.0/Factor, 1.0/Factor, 0, 0, \
HomMat2DGuide)
KappaGuide := Kappa*Factor*Factor
points_foerstner (Image1, 1, 2, 3, 200, 0.3, 'gauss', 'true', \
Rows1, Cols1, _, _, _, _, _, _, _, _)
points_foerstner (Image2, 1, 2, 3, 200, 0.3, 'gauss', 'true', \
Rows2, Cols2, _, _, _, _, _, _, _, _)
proj_match_points_distortion_ransac_guided (Image1, Image2, \
Rows1, Cols1, \
Rows2, Cols2, \
'ncc', 10, \
HomMat2DGuide, \
KappaGuide, 5, 0.5, \
'gold_standard', 2, 0, \
HomMat2D, Kappa, \
Error, Points1, Points2)
get_image_size (Image1, Width, Height)
CamParDist := [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)
points_foerstnerpoints_foerstnerPointsFoerstnerpoints_foerstnerPointsFoerstnerPointsFoerstner,
points_harrispoints_harrisPointsHarrispoints_harrisPointsHarrisPointsHarris
vector_to_proj_hom_mat2d_distortionvector_to_proj_hom_mat2d_distortionVectorToProjHomMat2dDistortionvector_to_proj_hom_mat2d_distortionVectorToProjHomMat2dDistortionVectorToProjHomMat2dDistortion,
change_radial_distortion_cam_parchange_radial_distortion_cam_parChangeRadialDistortionCamParchange_radial_distortion_cam_parChangeRadialDistortionCamParChangeRadialDistortionCamPar,
change_radial_distortion_imagechange_radial_distortion_imageChangeRadialDistortionImagechange_radial_distortion_imageChangeRadialDistortionImageChangeRadialDistortionImage,
change_radial_distortion_pointschange_radial_distortion_pointsChangeRadialDistortionPointschange_radial_distortion_pointsChangeRadialDistortionPointsChangeRadialDistortionPoints,
gen_binocular_proj_rectificationgen_binocular_proj_rectificationGenBinocularProjRectificationgen_binocular_proj_rectificationGenBinocularProjRectificationGenBinocularProjRectification,
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
proj_match_points_distortion_ransacproj_match_points_distortion_ransacProjMatchPointsDistortionRansacproj_match_points_distortion_ransacProjMatchPointsDistortionRansacProjMatchPointsDistortionRansac
proj_match_points_ransacproj_match_points_ransacProjMatchPointsRansacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansac,
proj_match_points_ransac_guidedproj_match_points_ransac_guidedProjMatchPointsRansacGuidedproj_match_points_ransac_guidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuided,
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
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.
Matching