New TIP paper accepted

Scale-invariant features and novel descriptors for omnidirectional images accepted for publications in IEEE Transactions on Image Processing. Scale Invariant Features and Polar Descriptors in Omnidirectional Images Zafer Arican and Pascal Frossard We propose a method to compute scale invariant features in omnidirectional images. We present a formulation based on Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that adapts to the mirror and lens structures in omnidirectional imaging. These operators lead to a scale-space analysis that preserves the geometry of the visual information in omnidirectional images. We then build a novel scale-invariant feature detection framework for omnidirectional images that can be mapped on the sphere. We further present a new descriptor and feature matching solution for these omnidirectional images. The descriptor builds on the log-polar planar descriptors and adapts the descriptor computation to the specific geometry and the non-uniform sampling density of omnidirectional images.