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International Journal of Computer Vision

Erschienen in:

01.11.2012

On Using Anisotropic Diffusion for Skeleton Extraction

verfasst von: Cem Direkoglu, Rozenn Dahyot, Michael Manzke

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2012

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Abstract

We present a novel and effective skeletonization algorithm for binary and gray-scale images, based on the anisotropic heat diffusion analogy. We diffuse the image in the direction normal to the feature boundaries and also allow tangential diffusion (curvature decreasing diffusion) to contribute slightly. The proposed anisotropic diffusion provides a high quality medial function in the image: it removes noise and preserves prominent curvatures of the shape along the level-sets (skeleton features). The skeleton strength map, which provides the likelihood of a point to be part of the skeleton, is defined by the mean curvature measure. Finally, thin and binary skeleton is obtained by non-maxima suppression and hysteresis thresholding of the skeleton strength map. Our method outperforms the most related and the popular methods in skeleton extraction especially in noisy conditions. Results show that the proposed approach is better at handling noise in images and preserving the skeleton features at the centerline of the shape.

Vorheriger Artikel Object Detection using Geometrical Context Feedback

Nächster Artikel Motion Coherent Tracking Using Multi-label MRF Optimization

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Titel: On Using Anisotropic Diffusion for Skeleton Extraction
verfasst von: Cem Direkoglu
Rozenn Dahyot
Michael Manzke
Publikationsdatum: 01.11.2012
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2012
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-012-0540-9

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