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Journal Articles International Journal of Computer Vision Year : 2007

Generalized Gradients: Priors on Minimization Flows


This paper tackles an important aspect of the variational problem underlying active contours: optimization by gradient flows. Classically, the definition of a gradient depends directly on the choice of an inner product structure. This consideration is largely absent from the active contours literature. Most authors, explicitely or implicitely, assume that the space of admissible deformations is ruled by the canonical L 2 inner prod-uct. The classical gradient flows reported in the literature are relative to this particular choice. Here, we investigate the relevance of using (i) other inner products, yielding other gradient descents, and (ii) other minimizing flows not deriving from any inner product. In particular, we show how to induce different de-grees of spatial consistency into the minimizing flow, in order to decrease the probability of getting trapped into irrelevant local minima. We report numerical experiments indicating that the sensitivity of the active contours method to initial conditions, which seriously limits its applicability and efficiency, is alleviated by our application-specific spatially coherent minimizing flows. We show that the choice of the inner product can be seen as a prior on the deformation fields and we present an extension of the definition of the gradient toward more general priors.
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hal-01117529 , version 1 (17-02-2015)



Guillaume Charpiat, Pierre Maurel, Jean-Philippe Pons, Renaud Keriven, Olivier Faugeras. Generalized Gradients: Priors on Minimization Flows. International Journal of Computer Vision, 2007, 73, pp.325 - 344. ⟨10.1007/s11263-006-9966-2⟩. ⟨hal-01117529⟩
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