Coordinate selection for affine invariant feature description
Christopher Bulla, Jens-Rainer Ohm · 2016
In this paper, we present a method for affine invariant feature description. Based on the gradient distribution of an image region we calculate two basis vectors defining an affine invariant coordinate system, used to normalize the image region. The estimated basis vectors are non-orthogonal and allow for a precise representation of the gradient distribution. The proposed method can be combined with any feature detector and descriptor. Its performance is evaluated on globally affine transformed as well as on real world images and compared to state of the art methods for affine invariant feature description. The observed results outperform the results obtained by the SIFT feature detector and are comparable to the results obtained by ASIFT while having less computational complexity and being more flexibly applicable in case of local affine modifications.