4.5 Article

Scale Selection Properties of Generalized Scale-Space Interest Point Detectors

期刊

JOURNAL OF MATHEMATICAL IMAGING AND VISION
卷 46, 期 2, 页码 177-210

出版社

SPRINGER
DOI: 10.1007/s10851-012-0378-3

关键词

Feature detection; Interest point; Blob detection; Corner detection; Scale; Scale-space; Scale selection; Scale invariance; Scale calibration; Scale linking; Feature trajectory; Deep structure; Affine transformation; Differential invariant; Gaussian derivative; Multi-scale representation; Computer vision

资金

  1. Swedish Research Council, Vetenskapsradet [2004-4680, 2010-4766]
  2. Royal Swedish Academy of Sciences
  3. Knut and Alice Wallenberg Foundation

向作者/读者索取更多资源

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 non-linear differential invariants and scale selection based on weighted averaging of scale values along feature trajectories over scale. 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 non-uniform 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.

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