Scale Space for Camera Invariant Features - Puig, Luis (IEEE transactions on pattern analysis and machine intelligence, 2014)
In this paper we propose a new approach to compute the scale space of any central projection system, such as catadioptric, fisheye or conventional cameras. Since these systems can be explained using a unified model, the single parameter that defines each type of system is used to automatically compute the corresponding Riemannian metric. This metric, is combined with the partial differential equations framework on manifolds, allows us to compute the Laplace-Beltrami (LB) operator, enabling the computation of the scale space of any central projection system. Scale space is essential for the intrinsic scale selection and neighborhood description in features like SIFT. We perform experiments with synthetic and real images to validate the generalization of our approach to any central projection system. We compare our approach with the best-existing methods showing competitive results in all type of cameras: catadioptric, fisheye, and perspective