Extending the concept of texture to the geometry of a mesh manifold surface is an emerging topic in image processing. This concept is different from gluing images to the surface, but rather indicates the presence of relief patterns that locally change the surface geometry, showing some regular and repetitive pattern. In this paper, we propose an efficient and effective framework to address this novel task, which encompasses the convolution operation and the casting of a variety of Local Binary Pattern on the mesh manifold. Results show that our technique outperforms the existing state-of-the-art methods in the challenging task of relief patterns classification.

Extending LBP and Convolution-like Operations on the Mesh / Claudio Tortorici, Naoufel Werghi, Stefano Berretti. - STAMPA. - (2019), pp. 4479-4483. ( IEEE International Conference on Image Processing Taipei, Taiwan 22-25 September 2019) [10.1109/ICIP.2019.8803593].

Extending LBP and Convolution-like Operations on the Mesh

Stefano Berretti
2019

Abstract

Extending the concept of texture to the geometry of a mesh manifold surface is an emerging topic in image processing. This concept is different from gluing images to the surface, but rather indicates the presence of relief patterns that locally change the surface geometry, showing some regular and repetitive pattern. In this paper, we propose an efficient and effective framework to address this novel task, which encompasses the convolution operation and the casting of a variety of Local Binary Pattern on the mesh manifold. Results show that our technique outperforms the existing state-of-the-art methods in the challenging task of relief patterns classification.
2019
IEEE International Conference on Image Processing
IEEE International Conference on Image Processing
Taipei, Taiwan
22-25 September 2019
Claudio Tortorici, Naoufel Werghi, Stefano Berretti
File in questo prodotto:
File Dimensione Formato  
icip19.pdf

Accesso chiuso

Descrizione: articolo principale
Tipologia: Versione finale referata (Postprint, Accepted manuscript)
Licenza: Tutti i diritti riservati
Dimensione 857.38 kB
Formato Adobe PDF
857.38 kB Adobe PDF   Richiedi una copia

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

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1175150
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 1
social impact