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Mixed Metric Random Forest for Dense Correspondence of Cone-Beam Computed Tomography Images.

, , , , , , and . MICCAI (1), volume 10433 of Lecture Notes in Computer Science, page 283-290. Springer, (2017)

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Mixed Metric Random Forest for Dense Correspondence of Cone-Beam Computed Tomography Images., , , , , , and . MICCAI (1), volume 10433 of Lecture Notes in Computer Science, page 283-290. Springer, (2017)Spatially Consistent Supervoxel Correspondences of Cone-Beam Computed Tomography Images., , , , , , and . IEEE Trans. Medical Imaging, 37 (10): 2310-2321 (2018)Unsupervised random forest for affinity estimation., , , , , and . Comput. Vis. Media, 8 (2): 257-272 (2022)Voxel-wise correspondence of cone-beam computed tomography images by cascaded randomized forest., , , , , and . ISBI, page 481-484. IEEE, (2017)Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images., , , , , , and . MLMI@MICCAI, volume 10541 of Lecture Notes in Computer Science, page 114-122. Springer, (2017)