Scale selection properties of generalized scalespace interest point detectorsTony LindebergJournal of Mathematical Imaging and Vision, volume 46, number 2, pages 177210, 2013. Digitally published with DOI:10.1007/s1085101203783 in September 2012. AbstractScaleinvariant interest points have found several highly successful applications in computer vision, in particular for imagebased matching and recognition.This paper presents a theoretical analysis of the scale selection properties of a generalized framework for detecting interest points from scalespace features presented in Lindeberg (Int. J. Comput. Vis. 2010, under revision) and comprising:
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 secondorder 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. PDF: (2.7 Mb) Online version: (At the official site of JMIV) Background and related material: (Integration with local image descriptors for image matching and object recognition) (Earlier paper on Laplacian and determinant of the Hessian interest points with automatic scale selection)
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