1-point RANSAC for Circular Motion Estimation in Computed Tomography (CT)
This work addresses a specific challenge in computed tomography for medical or industrial imaging, but it appears incremental as it builds on established RANSAC techniques.
The paper tackles the problem of estimating axial rotation angles from tomographic projections by proposing a RANSAC-based algorithm that uses a single correct keypoint correspondence, and it validates this method through experimental comparison against existing distribution-based approaches.
This paper proposes a RANSAC-based algorithm for determining the axial rotation angle of an object from a pair of its tomographic projections. An equation is derived for calculating the rotation angle using one correct keypoints correspondence of two tomographic projections. The proposed algorithm consists of the following steps: keypoints detection and matching, rotation angle estimation for each correspondence, outliers filtering with the RANSAC algorithm, finally, calculation of the desired angle by minimizing the re-projection error from the remaining correspondences. To validate the proposed method an experimental comparison against methods based on analysis of the distribution of the angles computed from all correspondences is conducted.