Retinal image registration through simultaneous camera pose and eye shape estimation.

In this paper, a retinal image registration method is proposed. The approach utilizes keypoint correspondences and assumes that the human eye has a spherical or ellipsoidal shape. The image registration problem amounts to solving a camera 3D pose estimation problem and, simultaneously, an eye 3D shape estimation problem. The camera pose estimation problem is solved by estimating the relative pose between the views from which the images were acquired. The eye shape estimation problem parameterizes the shape and orientation of an ellipsoidal model for the eye. Experimental evaluation shows 17.91% reduction of registration error and 47.52% reduction of the error standard deviation over state of the art methods.