dc.creator | Trevisi, Marco | es |
dc.creator | Carmona Galán, Ricardo | es |
dc.creator | Fernández Berni, Jorge | es |
dc.creator | Rodríguez Vázquez, Ángel Benito | es |
dc.date.accessioned | 2019-10-11T14:12:43Z | |
dc.date.available | 2019-10-11T14:12:43Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Trevisi, M., Carmona Galán, R., Fernández Berni, J. y Rodríguez Vázquez, Á.B. (2015). On the design of a sparsifying dictionary for compressive image feature extraction. En 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS) (689-692), El Cairo, Egipto: Institute of Electrical and Electronics Engineers. | |
dc.identifier.isbn | 978-1-5090-0246-7 | es |
dc.identifier.uri | https://hdl.handle.net/11441/89626 | |
dc.description.abstract | Compressive sensing is an alternative to Nyquist-rate sampling when the signal to be acquired is known to be sparse or compressible. A sparse signal has a small number of nonzero components compared to its total length. This property can either exist either in the sampling domain, i. e. time or space, or with respect to a transform basis. There is a parallel between representing a signal in a compressed domain and feature extraction. In both cases, there is an effort to reduce the amount of resources required to describe a large set of data. A given feature is often represented by a set of parameters, which only acquire a relevant value in a few points in the image plane. Although there are some works reported on feature extraction from compressed samples, none of them considers the implementation of the feature extractor as a part of the sensor itself. Our approach is to introduce a sparsifying dictionary, feasibly implementable at the focal plane, which describes the image in terms of features. This allows a standard reconstruction algorithm to directly recover the interesting image features, discarding the irrelevant information. In order to validate the approach, we have integrated a Harris-Stephens corner detector into the compressive sampling process. We have evaluated the accuracy of the reconstructed corners compared to applying the detector to a reconstructed image. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TEC2012-38921-C02, IPT-2011-1625-430000, IPC-20111009 | es |
dc.description.sponsorship | Junta de Andalucía TIC 2338-2013 | es |
dc.description.sponsorship | Office of Naval Research (USA) N000141410355 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Institute of Electrical and Electronics Engineers | es |
dc.relation.ispartof | 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS) (2015), p 689-692 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Compressive sampling | es |
dc.subject | Image feature extraction | es |
dc.subject | Sparse representation | es |
dc.title | On the design of a sparsifying dictionary for compressive image feature extraction | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo | es |
dc.relation.projectID | TEC2012-38921-C02 | es |
dc.relation.projectID | IPT-2011-1625-430000 | es |
dc.relation.projectID | IPC-20111009 | es |
dc.relation.projectID | TIC 2338-2013 | es |
dc.relation.projectID | N000141410355 | es |
dc.relation.publisherversion | https://doi.org/10.1109/ICECS.2015.7440410 | es |
dc.identifier.doi | 10.1109/ICECS.2015.7440410 | es |
idus.format.extent | 4 p. | es |
dc.publication.initialPage | 689 | es |
dc.publication.endPage | 692 | es |
dc.eventtitle | 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS) | es |
dc.eventinstitution | El Cairo, Egipto | es |
dc.identifier.sisius | 21210037 | es |