Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Clinically Viable Approach to Lung Segmentation and Nodules Reinclusion Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Recognizing Hand Configurations of Brazilian Sign Language Using Convolutional Neural Networks

verfasst von : A. S. Oliveria, C. F. F. Costa Filho, M. G. F. Costa

Erschienen in: XXVI Brazilian Congress on Biomedical Engineering

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper proposes evaluating three convolutional neural network architectures for recognizing hand configurations of the Brazilian Sign Language (Libras). To improve the generalization of neural networks, two techniques were employed: dropout and L2 regularization. A proprietary database consisting of 12.200 depth images, captured with the Kinect® sensor was used. Two hundred images were captured for each one of 61 Hand Configurations (HC) of Libras. The training and testing subsets were compounded using an interleave technique. An accuracy of 98% was achieved. This value is better than previous results obtained, with the same dataset, using the k-Nearest Neighbor (kNN) and Novelty classifiers, 95.41% and 96.31%, respectively.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Recognition of Libras Static Alphabet with MyoTM and Multi-Layer Perceptron
Nächstes Kapitel Reduction of Power Line Interference in ECG Using Adaptive Filters: A Comparative Study Between Different Adaptive Noise Cancellation Algorithms
Literatur
1.
Costa Filho, C.F.F., Santos, B.L., Souza, R.S., Santos, J.R., Costa, M.G.F.: A new method for recognizing hand configurations of Brazilian gesture language. In: 38th Annual International Conference of the IEEE-Engineering-in-Medicine-and- Biology-Society (EMBC), IEEE Engineering in Medicine and Biology Society Conference Proceedings, pp. 3829–3834 (2016)
2.
Costa Filho, C.F.F, Santos, B.L., Souza, R.S., Santos, J.R., Costa, M.G.F.: A fully automatic method for recognizing hand configurations of Brazilian Sign Language. Res. Biomed. Eng. 33(1), 78–89 (2017)
3.
Esobedo, E.C., Camara, G.C.: A new approach for dynamic gesture recognition using skeleton trajectory representation and histograms of cumulative magnitudes. In: 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 209–216 (2016)
4.
Escobedo, E.C., Camara, G.C.: A robust gesture recognition using hand local data and skeleton trajectory. In: IEEE International Conference on Image Processing (ICIP), pp. 1240–1244 (2015)
5.
Porfirio A.J., Wiggers K.L., Oliveira, L.E.S., Weingaertner, D.: Libras sign language hand configuration recognition based on 3D meshes. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Manchester, pp. 1588–1593 (2013)
6.
Almeida, S.G.M., Guimaraes, F.G., Ramirez, J.A.: Feature extraction in Brazilian sign language recognition based on phonological structure and using RGB-D sensors. Expert Syst. Appl. 41(16), 7259–7271 (2014)CrossRef
7.
Stiehl, D., Addams, L., Oliveira, L.S., Guimaraes, C., Brito Jr, A.S.: Towards a signwriting recognition system. In: 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 26–30 (2015)
8.
Sutton, Y.: A way to analyze American Sign Language and any other sign language without translation into any spoken language. In: National Symposium on Sign Language Research and Teaching (1980)
9.
Kang, B., Tripathi, S. Nguyen, T.Q.: Real-time sign language fingerspelling recognition using convolutional neural networks from depth map. In: 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 136–140 (2015)
10.
Zhu, X., Liu, W., Jia, X., Wong, K.Y.K.: A two-stage detector for hand detection in ego-centric videos. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–8 (2016)
11.
Pigou, L., Dieleman, S., Kindermans, P.J., Schrauwen, B.: Sign language recognition using convolutional neural networks. In: Computer Vision—ECCV 2014 Workshops. Lecture Notes in Computer Science, vol. 8925, pp. 572–578 (2015)
12.
Krizhevsky, A., Sutskever, I. and Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F. et al. (ed.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates (2012)
13.
Neris, M.N., Silva, A.J., Peres, S.M., Flores, F.C.: Self organizing maps and bit signature: a study applied on signal language recognition. In: IEEE International Joint Conference on Neural Networks, pp. 2934–2941 (2008)
14.
Carneiro, T.S., Cortez, P.C., Costa, R.C.S.: Recognizing Libras gestures with neural classifiers, using Hu invariant moments. In: Interaction 09—South America, São Paulo, pp. 190–195 (2009)
15.
Pizzolato, E.B., Anjo, M.S., Pedroso, G.C.: Automatic recognition of finger spelling for libras based on a two layer architecture. In: ACM Symposium on Applied Computing. Proceedings of the 25th Symposium on Applied Computing, pp. 970–974 (2010)
16.
Mehdi, S.A., Khan, Y.N.: Sign language recognition using sensor gloves. In: Proceedings of the 9th International Conference on IEEE Neural Information Processing, ICONIP’02, vol. 5, pp. 2204–2206 (2002)
17.
Wang, H.: Nearest neighbors by neighborhood counting. IEEE Trans. Pattern Anal. Mach. Intell. 28(6), 942–953 (2006)CrossRef
Metadaten
Titel
Recognizing Hand Configurations of Brazilian Sign Language Using Convolutional Neural Networks
verfasst von
A. S. Oliveria
C. F. F. Costa Filho
M. G. F. Costa
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
XXVI Brazilian Congress on Biomedical Engineering

Print ISBN: 978-981-13-2516-8

Electronic ISBN: 978-981-13-2517-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-13-2517-5_64

Neuer Inhalt

    Bildnachweise