Determination of the Attitude of 3D Objects from a Single Perspective View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projective pose estimation of linear and quadratic primitives in monocular computer vision
CVGIP: Image Understanding
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Computer-Integrated Surgery: Technology and Clinical Applications
Computer-Integrated Surgery: Technology and Clinical Applications
A Single Image Registration Method for CT Guided Interventions
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Pose reconstruction with an uncalibrated computed tomography imaging device
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Self-calibration of a general radially symmetric distortion model
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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The paper presents an algorithm for the automatic registration of a stereotactic frame from a single slice captured by a computed tomography (CT) scanner. Such registration is needed in interventional radiology for CT-image guidance of a robotic assistant. Stereotactic registration is based on a set of line fiducials that produce a set of image points. Our objective is to achieve the automatic registration of a stereotactic frame in the presence of noisy data and outliers. To this end, a new formulation of the stereotactic registration problem with a single image is proposed, for any configuration of the fiducials. With very few fiducials, closed-form and numerical solutions are presented. This comes in useful for building a robust automatic line to point matching algorithm based on the RANSAC statistical method. Simulation and experimental results are presented and highlight the robustness properties of the algorithm. They show that the registration is accurate with moderate computing requirements even for a large amount of outliers.