Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/114982
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dc.contributor.authorSánchez Nielsen,Maria Elenaen_US
dc.contributor.authorAntón Canalís,Luisen_US
dc.contributor.authorHernández Tejera, Francisco Marioen_US
dc.date.accessioned2022-06-07T07:23:17Z-
dc.date.available2022-06-07T07:23:17Z-
dc.date.issued2004en_US
dc.identifier.issn1213-6972en_US
dc.identifier.urihttp://hdl.handle.net/10553/114982-
dc.description.abstractEven after more than two decades of input devices development, many people still find the interaction with computers an uncomfortable experience. Efforts should be made to adapt computers to our natural means of communication: speech and body language. The PUI paradigm has emerged as a post-WIMP interface paradigm in order to cover these preferences. The aim of this paper is the proposal of a real time vision system for its application within visual interaction environments through hand gesture recognition, using general-purpose hardware and low cost sensors, like a simple personal computer and an USB web cam, so any user could make use of it in his office or home. The basis of our approach is a fast segmentation process to obtain the moving hand from the whole image, which is able to deal with a large number of hand shapes against different backgrounds and lighting conditions, and a recognition process that identifies the hand posture from the temporal sequence of segmented hands. The most important part of the recognition process is a robust shape comparison carried out through a Hausdorff distance approach, which operates on edge maps. The use of a visual memory allows the system to handle variations within a gesture and speed up the recognition process through the storage of different variables related to each gesture. This paper includes experimental evaluations of the recognition process of 26 hand postures and it discusses the results. Experiments show that the system can achieve a 90% recognition average rate and is suitable for real-time applicationsen_US
dc.languageengen_US
dc.relation.ispartofJournal of WSCG , Vol. 12en_US
dc.subject3304 Tecnología de los ordenadoresen_US
dc.subject330412 Dispositivos de controlen_US
dc.subject.otherMan-Machine Interactionen_US
dc.subject.otherPerceptual user interfaceen_US
dc.subject.otherImage Processingen_US
dc.subject.otherHand gesture recognitionen_US
dc.subject.otherHausdorff distanceen_US
dc.titleHand gesture recognition for human-machine interactionen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.description.lastpage9en_US
dc.description.firstpage1en_US
dc.relation.volume12en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.description.numberofpages9en_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INGen_US
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0001-9717-8048-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameSánchez Nielsen,Maria Elena-
crisitem.author.fullNameAntón Canalís, Luis-
crisitem.author.fullNameHernández Tejera, Francisco Mario-
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