Abstract Scale-invariant interest points have found several highly successful applications in computer vision, in particular for image-based matching and recognition. This paper presents a theoretical analysis of the scale selection properties of a generalized framework for detecting interest points from scale-space features presented in Lindeberg (Int. J. Comput. Vis. 2010, under revision) and comprising: -an enriched set of differential interest operators at a fixed scale including the Laplacian operator, the determinant of the Hessian, the new Hessian feature strength measures I and II and the rescaled level curve curvature operator, as well as -an enriched set of scale selection mechanisms including scale selection based on local extrema over scale, complementary post-smoothing after the computation of nonlinear differential invariants and scale selection based on weighted averaging of scale values along feature trajectories over scale. It is shown how the selected scales of different linear and non-linear interest point detectors can be analyzed for Gaussian blob models. Specifically it is shown that for a rotationally symmetric Gaussian blob model, the scale estimates obtained by weighted scale selection will be similar to the scale estimates obtained from local extrema over scale of The support from the Swedish Research Council, Vetenskapsrådet (contracts 2004Vetenskapsrådet (contracts -4680, 2010 and from the Royal Swedish Academy of Sciences as well as the Knut and Alice Wallenberg Foundation is gratefully acknowledged. Institute of Technology, 100 44 Stockholm, Sweden e-mail: tony@csc.kth.se scale normalized derivatives for each one of the pure secondorder operators. In this respect, no scale compensation is needed between the two types of scale selection approaches. When using post-smoothing, the scale estimates may, however, be different between different types of interest point operators, and it is shown how relative calibration factors can be derived to enable comparable scale estimates for each purely second-order operator and for different amounts of self-similar post-smoothing. A theoretical analysis of the sensitivity to affine image deformations is presented, and it is shown that the scale estimates obtained from the determinant of the Hessian operator are affine covariant for an anisotropic Gaussian blob model. Among the other purely second-order operators, the Hessian feature strength measure I has the lowest sensitivity to nonuniform scaling transformations, followed by the Laplacian operator and the Hessian feature strength measure II. The predictions from this theoretical analysis agree with experimental results of the repeatability properties of the different interest point detectors under affine and perspective transformations of real image data. A number of less complete results are derived for the level curve curvature operator.
In REF , he also gave a theoretic analysis of such scale selection mechanisms and showed that they are useful for other low level feature detection, such as interesting point detection.
Scale Selection Properties of Generalized Scale-Space Interest Point Detectors
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Computer Science Mathematics