In this work we propose, for the first time, to improve the performance of a Hand Pose Reconstruction (HPR) technique from RGBD camera data, which is affected by self-occlusions, leveraging upon postural synergy information, i.e., a priori information on how human most commonly use and shape their hands in everyday life tasks. More specifically, in our approach, we ignore joint angle values estimated with low confidence through a vision-based HPR technique and fuse synergistic information with such incomplete measures. Preliminary experiments are reported showing the effectiveness of the proposed integration.

Synergy-driven performance enhancement of vision-based 3D hand pose reconstruction

Ciotti, Simone;Battaglia, Edoardo;Bicchi, Antonio;Bianchi, Matteo
Supervision
2017-01-01

Abstract

In this work we propose, for the first time, to improve the performance of a Hand Pose Reconstruction (HPR) technique from RGBD camera data, which is affected by self-occlusions, leveraging upon postural synergy information, i.e., a priori information on how human most commonly use and shape their hands in everyday life tasks. More specifically, in our approach, we ignore joint angle values estimated with low confidence through a vision-based HPR technique and fuse synergistic information with such incomplete measures. Preliminary experiments are reported showing the effectiveness of the proposed integration.
2017
9783319588766
File in questo prodotto:
File Dimensione Formato  
Mobihealth.pdf

accesso aperto

Tipologia: Documento in Pre-print
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.92 MB
Formato Adobe PDF
1.92 MB Adobe PDF Visualizza/Apri

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/11568/919432
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact