Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/25796

TitleA comparative study of different image features for hand gesture machine learning
Author(s)Trigueiros, Paulo
Ribeiro, António Fernando
Reis, L. P.
KeywordsHand gesture recognition
Machine vision
Hand features
Hog
Fourier descriptors
Centroid distance
Radial signature
Shi-Thomasi corner detection
Shi-tomasi corner detection
Issue date18-Oct-2013
PublisherSCITEPRESS
Abstract(s)Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner. We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.
TypeConference paper
URIhttp://hdl.handle.net/1822/25796
ISBN9789898565389
Peer-Reviewedyes
AccessOpen access
Appears in Collections:DEI - Artigos em atas de congressos internacionais

Files in This Item:
File Description SizeFormat 
CI38.pdfDocumento principal634,81 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID