Segmentation by Elastica Energy with $L$<sup>1</sup> and $L$<sup>2</sup> Curvatures: A Performance Comparison
Year: 2019
Numerical Mathematics: Theory, Methods and Applications, Vol. 12 (2019), Iss. 1 : pp. 285–311
Abstract
In this paper, we propose an algorithm based on augmented Lagrangian method and give a performance comparison for two segmentation models that use the $L$1- and $L$2-Euler's elastica energy respectively as the regularization for image segmentation. To capture contour curvature more reliably, we develop novel augmented Lagrangian functionals that ensure the segmentation level set function to be signed distance functions, which avoids the reinitialization of segmentation function during the iterative process. With the proposed algorithm and with the same initial contours, we compare the performance of these two high-order segmentation models and numerically verify the different properties of the two models.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/nmtma.OA-2017-0051
Numerical Mathematics: Theory, Methods and Applications, Vol. 12 (2019), Iss. 1 : pp. 285–311
Published online: 2019-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 27