| 摘要 |
The present invention introduces repeatable keypoint detection for 2D images and 3D volumes, a novel way of descriptor formation for matching 2D keypoints and 3D keypoints and model estimation for registration up to an affinity under low feature matching accuracy. The invention relates to an affine registration estimation based on an algorithm, which requires three true positive matches of feature points and extend this to estimate the model based on only a single true positive match, which we call the one-point algorithm. In addition, handling image scale is also addressed by resolving the ambiguity of 2D image scale and 3D image scale. The Applicant demonstrates that 2D scale and 3D scale do not represent similar image and volume neighborhoods. The Applicant compares the inventive technique with state of the art global registration techniques, such as correlation based registration and indicate the superior performance of the inventive method which is several hundred times faster. |
