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Multi-scale Midline Extraction Using Creaseness

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Pattern Recognition and Image Analysis (ICAPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3687))

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Abstract

Applying the divergence operator on the gradient vector field is known as a robust method for computing the local creaseness, defined as the level set extrinsic curvature. Based on this measure, we present a multi-scale method to extract continuous midlines of elongated objects of various widths simultaneously. The scale-space is not built on the input image, but on the gradient vector field. During the iterative construction of the scale-space the current solution keeps thin objects even when they are located near more dominant structures. The representation of the midlines is realised as curves in the image plane, consisting of equidistant sample points. At each sample point the tangential direction of the curve is computed directly with the smoothed gradient vector field.

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© 2005 Springer-Verlag Berlin Heidelberg

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Rothaus, K., Jiang, X. (2005). Multi-scale Midline Extraction Using Creaseness. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_56

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  • DOI: https://doi.org/10.1007/11552499_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28833-6

  • Online ISBN: 978-3-540-31999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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