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Metrics of curves in shape optimization and analysis - Andrea Carlo ...

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10.9 New regularization methodsTypically, <strong>in</strong> active contour energies a length penalty is added to obta<strong>in</strong> regularity<strong>of</strong> the evolv<strong>in</strong>g contour:E(c) = E data (c) + α len(c) .It is important to note that this length penalty will regularize the curve onlyif the curve evolution is derived as a H 0 gradient descent flow. If another curvemetric is used to derive the gradient descent flow, then a priori the length penaltymay have no regulariz<strong>in</strong>g effect on the curve regularity (cf. 10.32).10.9.1 Elastic regularizationWe can now consider an alternative approach for regularity <strong>of</strong> curve:∫E(c) = E data (c) + α len(c) κ 2 (s) dswhere α > 0 is a fixed constant. This energy favors regularity <strong>of</strong> curve, but thisregularization does not rely on properties <strong>of</strong> the metric; <strong>and</strong> the regularizationis scale <strong>in</strong>variant. Note that the H 0 gradient flow <strong>of</strong> this energy is ill posed.The numerical results (orig<strong>in</strong>ally presented <strong>in</strong> [56]) are <strong>in</strong> Fig. 16. In allframes, the f<strong>in</strong>al limit <strong>of</strong> the gradient descent flow is shown; between differentframes, the value <strong>of</strong> α is <strong>in</strong>creased.c. . . . . . . . . . . .m<strong>in</strong>ima for α <strong>in</strong>creas<strong>in</strong>g → . . . . . . . . . . . .E data (c) + α len(c)(H 0 gradient descent)E data (c) +α len(c) ∫ c κ2 (s) ds(Sobolev gradient descent)Figure 16: Elastic regularization. (From [58] c○ 2008 IEEE. Reproduced with permission).88

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