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

Description

Given a set of coordinates of characteristic points (Cols1Cols1Cols1cols1cols_1,Rows1Rows1Rows1rows1rows_1) and (Cols2Cols2Cols2cols2cols_2,Rows2Rows2Rows2rows2rows_2) in both input images Image1Image1Image1image1image_1 and Image2Image2Image2image2image_2, and given a known approximation HomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_mat_2dguide for the transformation matrix between Image1Image1Image1image1image_1 and Image2Image2Image2image2image_2, proj_match_points_ransac_guidedproj_match_points_ransac_guidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuidedproj_match_points_ransac_guided automatically determines corresponding points and the homogeneous projective transformation matrix HomMat2DHomMat2DHomMat2DhomMat2Dhom_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_foerstnerPointsFoerstnerPointsFoerstnerpoints_foerstner or points_harrispoints_harrisPointsHarrisPointsHarrispoints_harris. The approximation HomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_mat_2dguide can, for example, be calculated with proj_match_points_ransacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansacproj_match_points_ransac on lower resolution versions of Image1Image1Image1image1image_1 and Image2Image2Image2image2image_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 MaskSizeMaskSizeMaskSizemaskSizemask_size x MaskSizeMaskSizeMaskSizemaskSizemask_size. Three metrics for the correlation can be selected. If GrayMatchMethodGrayMatchMethodGrayMatchMethodgrayMatchMethodgray_match_method 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_disparityBinocularDisparityBinocularDisparitybinocular_disparity. 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 MatchThresholdMatchThresholdMatchThresholdmatchThresholdmatch_threshold ('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 matching operations is limited based on the approximate transformation HomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_mat_2dguide. Only points within a distance of DistanceToleranceDistanceToleranceDistanceTolerancedistanceTolerancedistance_tolerance around the transformed a point in Image2Image2Image2image2image_2 of a point in Image1Image1Image1image1image_1 via HomMat2DGuideHomMat2DGuideHomMat2DGuidehomMat2DGuidehom_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 HomMat2DHomMat2DHomMat2DhomMat2Dhom_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 DistanceThresholdDistanceThresholdDistanceThresholddistanceThresholddistance_threshold.

Once a choice has been made, the matrix is further optimized using all consistent points. For this optimization, the EstimationMethodEstimationMethodEstimationMethodestimationMethodestimation_method can be chosen to either be the slow but mathematically optimal 'gold_standard'"gold_standard""gold_standard""gold_standard""gold_standard" method or the faster 'normalized_dlt'"normalized_dlt""normalized_dlt""normalized_dlt""normalized_dlt". Here, the algorithms of vector_to_proj_hom_mat2dvector_to_proj_hom_mat2dVectorToProjHomMat2dVectorToProjHomMat2dvector_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. Points1Points1Points1points1points_1 contains the indices of the matched input points from the first image, Points2Points2Points2points2points_2 contains the indices of the corresponding points in the second image.

The parameter RandSeedRandSeedRandSeedrandSeedrand_seed can be used to control the randomized nature of the RANSAC algorithm, and hence to obtain reproducible results. If RandSeedRandSeedRandSeedrandSeedrand_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 RandSeedRandSeedRandSeedrandSeedrand_seed = 0, the random number generator is initialized with the current time. Hence, the results may not be reproducible in this case. The value set for the HALCON system variable 'seed_rand'"seed_rand""seed_rand""seed_rand""seed_rand" (see set_systemset_systemSetSystemSetSystemset_system) does not affect the results of proj_match_points_ransac_guidedproj_match_points_ransac_guidedProjMatchPointsRansacGuidedProjMatchPointsRansacGuidedproj_match_points_ransac_guided.

Execution Information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

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_foerstnerPointsFoerstnerPointsFoerstnerpoints_foerstner, points_harrispoints_harrisPointsHarrisPointsHarrispoints_harris

Possible Successors

projective_trans_imageprojective_trans_imageProjectiveTransImageProjectiveTransImageprojective_trans_image, projective_trans_image_sizeprojective_trans_image_sizeProjectiveTransImageSizeProjectiveTransImageSizeprojective_trans_image_size, projective_trans_regionprojective_trans_regionProjectiveTransRegionProjectiveTransRegionprojective_trans_region, projective_trans_contour_xldprojective_trans_contour_xldProjectiveTransContourXldProjectiveTransContourXldprojective_trans_contour_xld, projective_trans_point_2dprojective_trans_point_2dProjectiveTransPoint2dProjectiveTransPoint2dprojective_trans_point_2d, projective_trans_pixelprojective_trans_pixelProjectiveTransPixelProjectiveTransPixelprojective_trans_pixel

Alternatives

hom_vector_to_proj_hom_mat2dhom_vector_to_proj_hom_mat2dHomVectorToProjHomMat2dHomVectorToProjHomMat2dhom_vector_to_proj_hom_mat2d, vector_to_proj_hom_mat2dvector_to_proj_hom_mat2dVectorToProjHomMat2dVectorToProjHomMat2dvector_to_proj_hom_mat2d

See also

proj_match_points_ransacproj_match_points_ransacProjMatchPointsRansacProjMatchPointsRansacproj_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

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