Abstract. We propose a sparsity-inducing multi-channel multiple region model for the efficient partitioning of a mesh into salient parts. Our approach is based on rewriting the Mumford-Shah models in terms of piece-wise smooth/constant functionals that incorporate a non-convex regularizer for minimizing the boundary lengths. The solution of this optimization problem, obtained by an efficient proximal forward backward algorithm, is used by a simple thresholding/clusterization procedure to segment the shape into the required number of parts. Therefore, it is not necessary to further solve the optimization problem for a different number of partitioning regions. Experimental results show the effectiveness and efficiency of our proposals when applied to both single- and multi-channel (shape characterizing) functions.

Sparsity-inducing variational shape partitioning

HUSKA, MARTIN;MORIGI, SERENA
2017

Abstract

Abstract. We propose a sparsity-inducing multi-channel multiple region model for the efficient partitioning of a mesh into salient parts. Our approach is based on rewriting the Mumford-Shah models in terms of piece-wise smooth/constant functionals that incorporate a non-convex regularizer for minimizing the boundary lengths. The solution of this optimization problem, obtained by an efficient proximal forward backward algorithm, is used by a simple thresholding/clusterization procedure to segment the shape into the required number of parts. Therefore, it is not necessary to further solve the optimization problem for a different number of partitioning regions. Experimental results show the effectiveness and efficiency of our proposals when applied to both single- and multi-channel (shape characterizing) functions.
2017
Huska, Martin ; Morigi, Serena
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/589683
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact