Increasing the precision of junction shaped features

The scale invariant feature operator (SFOP) detects circular features from an image using a spiral shape model. Special cases of the spiral model are junctions and circular symmetric shapes. The spatial localization is determined with subpixel accuracy which is obtained by an interpolation of the structure tensor in the scale space. For the interpolation, SFOP uses a 3D quadratic function. This leads to suboptimal solutions since the structure tensor surrounding a feature does not show the shape of a 3D quadratic. The aim of this paper is to improve the localization of the features detected by SFOP. A Difference of Gaussians function is proposed for the signal approximation which leads to improved precision values and to more accurate features. The proposed method improves the localization such that 72.5% of the features increase their precision. Hence, more features are extracted while increasing their repeatability by up to 9% on standard benchmarks.