Scale selection for anisotropic scale-space: application to...

Image analysis – Applications – Biomedical applications

Reexamination Certificate

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C702S189000

Reexamination Certificate

active

07616792

ABSTRACT:
A method for determining a structure in volumetric data includes determining an anisotropic scale-space for a local region around a given spatial local maximum, determining L-normalized scale-space derivatives in the anisotropic scale-space, and determining the presence of noise in the volumetric data and upon determining noise in the volumetric data, determining the structure by a most-stable-over-scales determination, and upon determining noise below a desirable level, determining the structure by one of the most-stable-over-scales determination and a maximum-over-scales determination.

REFERENCES:
patent: 2003/0068082 (2003-04-01), Comaniciu et al.
patent: 1225544 (2002-07-01), None
Laptev et al., “Space-time Interest Points”, Oct. 2003.
Lindeberg et al., “Real-Time Scale Selection in Hybrid Multi-scale Representations”, Jun. 2003.
Lindeberg, “Feature Detection with Automatic Scale Selection”, 1998.
Okada Kazunori et al: “Scale selection for an anisotropic scale-space: Application to volumetric tumor characterization” Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit; Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Patten Recognit, vol. 1, 2004, pp. 1594-1601, XP002322547 the whole document.
Tony Lindeberg: “Scale-Space Theory in Computer Vision” 1994, Kluwer Academic Publishers, XP002323529 Chapter 15: “Non-uniform” smoothing.

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