Mostra el registre d'ítem simple
Top-down model fitting for hand pose recovery in sequences of depth images
dc.contributor.author | Madadi, Meysam |
dc.contributor.author | Escalera, Sergio |
dc.contributor.author | Carruesco Llorens, Àlex |
dc.contributor.author | Andújar Gran, Carlos Antonio |
dc.contributor.author | Baró, Xavier |
dc.contributor.author | Gonzàlez, Jordi |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2019-03-07T08:44:42Z |
dc.date.available | 2020-09-21T00:27:57Z |
dc.date.issued | 2018-11 |
dc.identifier.citation | Madadi, M. [et al.]. Top-down model fitting for hand pose recovery in sequences of depth images. "Image and vision computing", Novembre 2018, vol. 79, p. 63-75. |
dc.identifier.issn | 0262-8856 |
dc.identifier.uri | http://hdl.handle.net/2117/130123 |
dc.description.abstract | State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs. |
dc.format.extent | 13 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Infografia |
dc.subject.lcsh | Three dimensional imaging |
dc.subject.other | Hand pose recovery |
dc.subject.other | Shape description |
dc.subject.other | Depth image |
dc.subject.other | Hand segmentation |
dc.subject.other | Temporal modeling |
dc.title | Top-down model fitting for hand pose recovery in sequences of depth images |
dc.type | Article |
dc.subject.lemac | Infografia tridimensional |
dc.contributor.group | Universitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica |
dc.identifier.doi | 10.1016/j.imavis.2018.09.006 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0262885618301513 |
dc.rights.access | Open Access |
local.identifier.drac | 23501932 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Madadi, M.; Escalera, S.; Carruesco, A.; Andújar, C.; Baró, X.; Gonzàlez, J. |
local.citation.publicationName | Image and vision computing |
local.citation.volume | 79 |
local.citation.startingPage | 63 |
local.citation.endingPage | 75 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [1.049]
-
Articles de revista [95]