Tracking of Lines in Spherical Images via Sub-Riemannian Geodesics in .

In order to detect salient lines in spherical images, we consider the problem of minimizing the functional for a curve on a sphere with fixed boundary points and directions. The total length is free, denotes the spherical arclength, and denotes the geodesic curvature of  . Here the smooth external cost is obtained from spherical data. We lift this problem to the sub-Riemannian (SR) problem in Lie group and show that the spherical projection of certain SR geodesics provides a solution to our curve optimization problem. In fact, this holds only for the geodesics whose spherical projection does not exhibit a cusp. The problem is a spherical extension of a well-known contour perception model, where we extend the model by Boscain and Rossi to the general case . For , we derive SR geodesics and evaluate the first cusp time. We show that these curves have a simpler expression when they are parameterized by spherical arclength rather than by sub-Riemannian arclength. For case (data-driven SR geodesics), we solve via a SR Fast Marching method. Finally, we show an experiment of vessel tracking in a spherical image of the retina and study the effect of including the spherical geometry in analysis of vessels curvature.