dc.contributorOlivares Poggi, Cesar Augusto
dc.creatorHuiza Pereyra, Eric Raphael
dc.date2020-09-01T00:12:05Z
dc.date2020-09-01T00:12:05Z
dc.date2020
dc.date2020-08-31
dc.date.accessioned2023-03-09T04:02:12Z
dc.date.available2023-03-09T04:02:12Z
dc.identifierhttp://hdl.handle.net/20.500.12404/16906
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6010736
dc.descriptionPeople with deafness or hearing disabilities who aim to use computer based systems rely on state-of-art video classification and human action recognition techniques that combine traditional movement pat-tern recognition and deep learning techniques. In this work we present a pipeline for semi-automatic video annotation applied to a non-annotated Peru-vian Signs Language (PSL) corpus along with a novel method for a progressive detection of PSL elements (nSDm). We produced a set of video annotations in-dicating signs appearances for a small set of nouns and numbers along with a labeled PSL dataset (PSL dataset). A model obtained after ensemble a 2D CNN trained with movement patterns extracted from the PSL dataset using Lucas Kanade Opticalflow, and a RNN with LSTM cells trained with raw RGB frames extracted from the PSL dataset reporting state-of-art results over the PSL dataset on signs classification tasks in terms of AUC, Precision and Recall.
dc.descriptionTrabajo de investigación
dc.languageeng
dc.publisherPontificia Universidad Católica del Perú
dc.publisherPE
dc.rightsAtribución 2.5 Perú
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by/2.5/pe/
dc.subjectRedes neuronales (Computación)
dc.subjectAlgoritmos computacionales
dc.subjectReconocimiento óptico de patrones
dc.subjecthttps://purl.org/pe-repo/ocde/ford#1.02.00
dc.titleTalking with signs: a simple method to detect nouns and numbers in a non annotated signs language corpus
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typeTesis de maestría


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