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Scanpath and saliency prediction on 360 degree images
dc.contributor.author | Assens Reina, Marc |
dc.contributor.author | Giró Nieto, Xavier |
dc.contributor.author | McGuinness, Kevin |
dc.contributor.author | O'Connor, Noel |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2018-07-16T08:16:15Z |
dc.date.available | 2022-06-01T00:36:08Z |
dc.date.issued | 2018-06-23 |
dc.identifier.citation | Assens, M., Giro, X., McGuinness, K., O'Connor, N. Scanpath and saliency prediction on 360 degree images. "Signal processing: image communication", 23 Juny 2018, vol. 69, p. 8-14. |
dc.identifier.issn | 0923-5965 |
dc.identifier.other | https://imatge.upc.edu/web/publications/scanpath-and-saliency-prediction-360-degree-images |
dc.identifier.uri | http://hdl.handle.net/2117/119346 |
dc.description.abstract | We introduce deep neural networks for scanpath and saliency prediction trained on 360-degree images. The scanpath prediction model called SaltiNet is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation using a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. We also show how a similar architecture achieves state-of-the-art performance for the related task of saliency map prediction. Our source code and trained models available at https://github.com/massens/saliency-360salient-2017. |
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::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
dc.subject.lcsh | Image processing--Digital techniques |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Artificial intelligence |
dc.subject.lcsh | Three-dimensional imaging |
dc.subject.lcsh | Machine learning |
dc.subject.other | deep learning |
dc.subject.other | machine learning |
dc.subject.other | saliency |
dc.subject.other | scanpath |
dc.subject.other | visual attention |
dc.title | Scanpath and saliency prediction on 360 degree images |
dc.type | Article |
dc.subject.lemac | Imatges -- Processament -- Tècniques digitals |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Intel·ligència artificial |
dc.subject.lemac | Imatges tridimensionals |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
dc.identifier.doi | 10.1016/j.image.2018.06.006 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0923596518306209 |
dc.rights.access | Open Access |
local.identifier.drac | 23234728 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/2PE/TEC2016-75976-R |
local.citation.author | Assens, M.; Giro, X.; McGuinness, K.; O'Connor, N. |
local.citation.publicationName | Signal processing: image communication |
local.citation.volume | 69 |
local.citation.startingPage | 8 |
local.citation.endingPage | 14 |
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