IEEE Trans Image Process - Scale-invariant features and polar descriptors in omnidirectional imaging.

Tópicos

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Resumo

We propose a method to compute scale-invariant features in omnidirectional images. We present a formulation based on the Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that adapt 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 nonuniform sampling density of omnidirectional images. We also propose a rotation-invariant matching method that eliminates the orientation computation during the feature detection phase and thus decreases the computational complexity. Experimental results demonstrate that the new feature computation method combined with the adapted descriptors offers promising detection and matching performance, i.e., it improves on the common scale-invariant feature transform (SIFT) features computed on the unwrapped omnidirectional images, as well as spherical SIFT features. Finally, we show that the proposed framework also permits to match features between images with different native geometry.

Resumo Limpo

propos method comput scaleinvari featur omnidirect imag present formul base riemannian geometri definit differenti oper noneuclidian manifold adapt mirror len structur omnidirect imag oper lead scalespac analysi preserv geometri visual inform omnidirect imag build novel scaleinvari featur detect framework omnidirect imag can map sphere present new descriptor featur match solut omnidirect imag descriptor build logpolar planar descriptor adapt descriptor comput specif geometri nonuniform sampl densiti omnidirect imag also propos rotationinvari match method elimin orient comput featur detect phase thus decreas comput complex experiment result demonstr new featur comput method combin adapt descriptor offer promis detect match perform ie improv common scaleinvari featur transform sift featur comput unwrap omnidirect imag well spheric sift featur final show propos framework also permit match featur imag differ nativ geometri

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