Scale Invariant Feature Transform on the Sphere: Theory and Applications

By Javier Cruz-Mota, Iva Bogdanova, Benoît Paquier, Michel Bierlaire, Jean-Philippe Thiran. 2011

182 citations recorded by the source corpus.

A SIFT algorithm in spherical coordinates for omnidirectional images is proposed. This algorithm can generate two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between two omnidirectional images can be performed, and with the second ones, the same matching process can be done but between omnidirectional and planar images. Furthermore, a planar to spherical mapping is introduced and an algorithm for its estimation is given. This mapping allows to extract objects from an omnidirectional image given their SIFT descriptors in a planar image. Several experiments, confirming the promising and accurate performance of the system, are conducted.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network (2018, neutral): “Learning images on the sphere in a rotation invariant way has applications in a wide range of domains from 360 degree video through drone navigation to molecular chemistry [9, 10, 11, 12, 13, 14, 15].”
Graph-Based Classification of Omnidirectional Images (2017, methodology): “Contrary to the previous approaches, the methods in [3, 27, 28] design a scale invariant SIFT descriptor for the wide-angle cameras based on the result of the work in [21] that introduced a bijection mapping between omnidirectional images and a spherical surface.”