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Segmentation of color images using mean curvature flow and parametric curves

  • * Corresponding author: Petr Pauš

    * Corresponding author: Petr Pauš 
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  • Automatic detection of objects in photos and images is beneficial in various scientific and industrial fields. This contribution suggests an algorithm for segmentation of color images by the means of the parametric mean curvature flow equation and CIE94 color distance function. The parametric approach is enriched by the enhanced algorithm for topological changes where the intersection of curves is computed instead of unreliable curve distance. The result is a set of parametric curves enclosing the object. The algorithm is presented on a test image and also on real photos.

    Mathematics Subject Classification: Primary: 68U05, 65D18; Secondary: 14H50.


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  • Figure 1.  Algorithm for topological changes of a closed curve $ \Gamma $ which overlaps itself under the external force. The intersections are computed and the overlapping segments of the curve are removed. The resulting two closed curves continue evolution in time

    Figure 2.  Original image with white background (left), gray-scale intensity image from a red color (middle), and simple conversion to gray-scale and inversion (right)

    Figure 3.  Artificial color image segmentation with the red reference color

    Figure 4.  Comparison of the color distance segmentation (left) and simple gray-scale conversion segmentation (right)

    Figure 5.  Original yellow flower photo (left), the distance image from a yellow color (middle), and a simple conversion to gray-scale (right)

    Figure 6.  Comparison of the color distance segmentation (left) and simple gray-scale conversion segmentation (right) for a yellow flower

    Figure 7.  The photo of a cloud and its distance image from the almost white (very light light blue) color

    Figure 8.  Segmentation of the original cloud image

    Figure 9.  Segmentation of the sunflower with different shades of yellow

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    [6] S. Osher and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces, Applied Mathematical Sciences, 153. Springer-Verlag, New York, 2003. doi: 10.1007/b98879.
    [7] P. Pauš and M. Beneš, Direct approach to mean-curvature flow with topological changes, Kybernetika (Prague), 45 (2009), 591-604. 
    [8] P. Pauš and M. Beneš, Algorithm for topological changes of parametrically described curves, Proceedings of ALGORITMY, (2009), 176–184.
    [9] P. Pauš and S. Yazaki, Exact solution for dislocation bowing and a posteriori numerical technique for dislocation touching-splitting, JSIAM Letters, 7 (2015), 57-60.  doi: 10.14495/jsiaml.7.57.
    [10] D. Ševčovič, Qualitative and quantitative aspects of curvature driven flows of planar curves, Topics on Partial Differential Equations, Jindřich Nečas Cent. Math. Model. Lect. Notes, MatFyzPress, Prague, 2 (2007), 55–119.
    [11] D. Ševčovič and S. Yazaki, Evolution of plane curves with a curvature adjusted tangential velocity, Japan Journal of Industrial and Applied Mathematics, 28 (2011), 413-442.  doi: 10.1007/s13160-011-0046-9.
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