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.
In this work, we study the performance of wide-used keypoints detection and description algorithms (SIFT, SURF, ORB, BRISK, AKAZE) which were originally developed for images taken in visible light but widely applied in the fields where images are taken in a different spectrum. We compare the quality of algorithms and their robustness to various image transformations. The algorithms' performance is tested on two image sets in the different spectra: digital X-Ray images and images taken in the visible spectrum. Each dataset captures complex scenes with many objects and partial occlusions. Geometrical transformations (rotation, shearing, scaling), linear color transformations, Gaussian blur are applied to the images. Then the detection and description algorithms are tested on the original and transformed images. The repeatability and number of corresponding points are calculated to assess detection algorithms. The ratio of correctly matched descriptors together with the ratio of the distances between the query descriptor, the nearest descriptor, and the second matched descriptor is computed to evaluate descriptors' quality. The algorithms showed different behavior on different spectra. SURF demonstrated to be the best X-ray keypoint detector and for the visible spectrum, it shares first place with AKAZE detector. SIFT is the best descriptor in both spectra. The strong and weak points of each algorithm are discussed in the paper.
We present the collections of images of the same rotating plastic object made in X-ray and visible spectra. Both parts of the dataset contain 400 images. The images are maid every 0.5 degrees of the object axial rotation. The collection of images is designed for evaluation of the performance of circular motion estimation algorithms as well as for the study of X-ray nature influence on the image analysis algorithms such as keypoints detection and description. The dataset is available at https://github.com/Visillect/xvcm-dataset.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.