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Skeleton-based morphological shape comparison

  • Representation, Processing, Analysis and Understanding of Images
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Abstract

The Pytiev morphological shape comparison technique is extended for Serra mathematical morphology. Instead of a constant set of connected regions with variable intensity, a constant morphological skeleton with a variable radial (scale) function is applied as an image shape descriptor. This allows projecting binary patterns onto shapes of other patterns and calculating the respective morphological correlation coefficients.

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References

  1. Yu. Vizilter and S. Zheltov, “Projective Morphologies and Their Application in Structural Analysis of Digital Images,” Comp. Syst. Sci. 47(6), 944–958 (2008).

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  2. Yu. Pyt’ev, “Morphological Image Analysis,” Pattern Recogn. Image Anal. 3, Nos. 19–28 (1993).

  3. J. Serra, Image Analysis and Mathematical Morphology (Acad. Press, London, 1982).

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  4. L. Mestetskiy and A. Semenov, “Binary Image Skeleton—Continuous Approach,” in Proc. 3rd Int. Conf. on Computer Vision Theory and Applications (VISAPP 2008) (Funchal, 2008), Vol. 1, pp. 251–258.

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Correspondence to Y. V. Vizilter.

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Vizilter, Y.V., Sidyakin, S.V., Rubis, A.Y. et al. Skeleton-based morphological shape comparison. Pattern Recognit. Image Anal. 21, 357–360 (2011). https://doi.org/10.1134/S1054661811021136

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  • DOI: https://doi.org/10.1134/S1054661811021136

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