Um sistema de informação extensível para o reconhecimento automático de LIBRAS
Resumo
Este artigo apresenta um sistema de informação para o reconhecimento automático de LIBRAS, fundamentado em dois pilares: um ambiente configurável e extensível para o gerenciamento de experimentos de processamento de línguas de sinais, baseado no uso de workflows científicos e um conjunto de modulos desenvolvidos especificamente para o processamento de imagens é vídeos, composto por metodos para a segmentação e classificação de imagens.
Palavras-chave:
sistema de informação, reconhecimento automático, Libras
Referências
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Caridakis, G., Diamanti, O., Karpouzis, K., e Maragos, P. (2008). Automatic sign language recognition: vision based feature extraction and probabilistic recognition scheme from multiple cues. In Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments, PETRA ’08, pages 89:1–89:8, New York, NY, USA. ACM.
Digiampietri, L. A., Perez-Alcázar, J. J., e Freitas, R. S., Araújo, J. C.,éric H. Ostroski,é Santiago, C. R. N. (2011). Uso de planejamento em inteligencia artificial para o desenvolvimento automático de software. Ináutonomous Software Systems (AutoSoft 2011), page 10.
Fang, G., Gao, W., e Zhao, D. (2003). Large vocabulary sign language recognition based on hierarchical decision trees. In Proceedings of the 5th international conference on Multimodal interfaces, ICMI ’03, pages 125–131, New York, NY, USA. ACM.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., e Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explorations, 11(1):10–18.
Holt, G. A. T., Doorn, A. J. V., Reinders, M. J. T., Hendriks, E. A., e Ridder, H. D. (2011). Human-inspired search for redundancy in automatic sign language recognition. ACM Trans. Appl. Percept., 8:15:1–15:15.
Huang, Z., Jiang, D., e Zhao, W. (2010). Study of sign language recognition based on gabor wavelet transforms. In Computer Design and Applications (ICCDA), 2010 International Conference on, volume 1, pages V1–151 –V1–154.
Jiangqin, W., wen, G., yibo, S., wei, L., e bo, P. (1998). A simple sign language recognition system based on data glove. In Signal Processing Proceedings, 1998. ICSP ’98. 1998 Fourth International Conference on, volume 2, pages 1257 –1260 vol.2.
Klima, E. e Bellugi, U. (1979). The signs of language. Cambridge University Press.
Kumarage, D., Fernando, S., Fernando, P., Madushanka, D., e Samarasinghe, R. (2011). Real-time sign language gesture recognition using still-image comparison amp; motion recognition. In Industrial and Information Systems (ICIIS), 2011 6th IEEE International Conference on, pages 169 –174.
Y., Chen, X., Tian, J., Zhang, X., Wang, K., e Yang, J. (2010). Automatic recognition of sign language subwords based on portable accelerometer and emg sensors. In International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ICMI-MLMI ’10, pages 17:1–17:7, New York, NY, USA. ACM.
Maebatake, M., Suzuki, I., Nishida, M., Horiuchi, Y., e Kuroiwa, S. (2008). Sign language recognition based on position and movement using multi-stream hmm. In Proceedings of the 2008 Second International Symposium on Universal Communication, ISUC ’08, pages 478–481, Washington, DC, USA. IEEE Computer Society.
Medeiros, C., Perez-Alcazar, J., Digiampietri, L., Pastorello, G., Santanche, A., Torres, R., Madeira, E., e Bacarin, E. (2005). WOODSS and the Web: Annotating and Reusing Scientific Workflows. ACM SIGMOD Record, 34(3):18–23.
Michael, N., Metaxas, D., e Neidle, C. (2009). Spatial and temporal pyramids for grammatical expression recognition of american sign language. In Proceedings of the 11thinternational ACM SIGACCESS conference on Computers and accessibility, Assets ’09, pages 75–82, New York, NY, USA. ACM.
Quan, Y. (2010). Chinese sign language recognition based on video sequence appearance modeling. In Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, pages 1537 –1542.
Stokoe, W. C. (2005). Sign language structure: An outline of the visual communication systems of the american deaf. Journal of Deaf Studies and Deaf Education, 10(1):3–37.
Theodorakis, S., Katsamanis, A., e Maragos, P. (2009). Product-hmms for automatic sign language recognition. In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, pages 1601 –1604.
Valença, A. C. (2010). Uso de mineraçao de dados para aperfeiçoar sistemas de recuperaçao de imagens por conteudo. Technical report, Universidade de São Paulo.
Zhang, L.-G., Chen, X., Wang, C., Chen, Y., e Gao, W. (2005). Recognition of sign language subwords based on boosted hidden markov models. In Proceedings of the 7th international conference on Multimodal interfaces, ICMI ’05, pages 282–287, New York, NY, USA. ACM.
Zhang, L.-G., Chen, Y., Fang, G., Chen, X., e Gao, W. (2004). A vision-based sign language recognition system using tied-mixture density hmm. In Proceedings of the 6th international conference on Multimodal interfaces, ICMI ’04, pages 198–204, New York, NY, USA. ACM.
Bauer, B. e Hienz, H. (2000). Relevant features for video-based continuous sign language recognition. In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on, pages 440 –445.
Caridakis, G., Diamanti, O., Karpouzis, K., e Maragos, P. (2008). Automatic sign language recognition: vision based feature extraction and probabilistic recognition scheme from multiple cues. In Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments, PETRA ’08, pages 89:1–89:8, New York, NY, USA. ACM.
Digiampietri, L. A., Perez-Alcázar, J. J., e Freitas, R. S., Araújo, J. C.,éric H. Ostroski,é Santiago, C. R. N. (2011). Uso de planejamento em inteligencia artificial para o desenvolvimento automático de software. Ináutonomous Software Systems (AutoSoft 2011), page 10.
Fang, G., Gao, W., e Zhao, D. (2003). Large vocabulary sign language recognition based on hierarchical decision trees. In Proceedings of the 5th international conference on Multimodal interfaces, ICMI ’03, pages 125–131, New York, NY, USA. ACM.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., e Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explorations, 11(1):10–18.
Holt, G. A. T., Doorn, A. J. V., Reinders, M. J. T., Hendriks, E. A., e Ridder, H. D. (2011). Human-inspired search for redundancy in automatic sign language recognition. ACM Trans. Appl. Percept., 8:15:1–15:15.
Huang, Z., Jiang, D., e Zhao, W. (2010). Study of sign language recognition based on gabor wavelet transforms. In Computer Design and Applications (ICCDA), 2010 International Conference on, volume 1, pages V1–151 –V1–154.
Jiangqin, W., wen, G., yibo, S., wei, L., e bo, P. (1998). A simple sign language recognition system based on data glove. In Signal Processing Proceedings, 1998. ICSP ’98. 1998 Fourth International Conference on, volume 2, pages 1257 –1260 vol.2.
Klima, E. e Bellugi, U. (1979). The signs of language. Cambridge University Press.
Kumarage, D., Fernando, S., Fernando, P., Madushanka, D., e Samarasinghe, R. (2011). Real-time sign language gesture recognition using still-image comparison amp; motion recognition. In Industrial and Information Systems (ICIIS), 2011 6th IEEE International Conference on, pages 169 –174.
Y., Chen, X., Tian, J., Zhang, X., Wang, K., e Yang, J. (2010). Automatic recognition of sign language subwords based on portable accelerometer and emg sensors. In International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ICMI-MLMI ’10, pages 17:1–17:7, New York, NY, USA. ACM.
Maebatake, M., Suzuki, I., Nishida, M., Horiuchi, Y., e Kuroiwa, S. (2008). Sign language recognition based on position and movement using multi-stream hmm. In Proceedings of the 2008 Second International Symposium on Universal Communication, ISUC ’08, pages 478–481, Washington, DC, USA. IEEE Computer Society.
Medeiros, C., Perez-Alcazar, J., Digiampietri, L., Pastorello, G., Santanche, A., Torres, R., Madeira, E., e Bacarin, E. (2005). WOODSS and the Web: Annotating and Reusing Scientific Workflows. ACM SIGMOD Record, 34(3):18–23.
Michael, N., Metaxas, D., e Neidle, C. (2009). Spatial and temporal pyramids for grammatical expression recognition of american sign language. In Proceedings of the 11thinternational ACM SIGACCESS conference on Computers and accessibility, Assets ’09, pages 75–82, New York, NY, USA. ACM.
Quan, Y. (2010). Chinese sign language recognition based on video sequence appearance modeling. In Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, pages 1537 –1542.
Stokoe, W. C. (2005). Sign language structure: An outline of the visual communication systems of the american deaf. Journal of Deaf Studies and Deaf Education, 10(1):3–37.
Theodorakis, S., Katsamanis, A., e Maragos, P. (2009). Product-hmms for automatic sign language recognition. In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, pages 1601 –1604.
Valença, A. C. (2010). Uso de mineraçao de dados para aperfeiçoar sistemas de recuperaçao de imagens por conteudo. Technical report, Universidade de São Paulo.
Zhang, L.-G., Chen, X., Wang, C., Chen, Y., e Gao, W. (2005). Recognition of sign language subwords based on boosted hidden markov models. In Proceedings of the 7th international conference on Multimodal interfaces, ICMI ’05, pages 282–287, New York, NY, USA. ACM.
Zhang, L.-G., Chen, Y., Fang, G., Chen, X., e Gao, W. (2004). A vision-based sign language recognition system using tied-mixture density hmm. In Proceedings of the 6th international conference on Multimodal interfaces, ICMI ’04, pages 198–204, New York, NY, USA. ACM.
Publicado
16/05/2012
Como Citar
DIGIAMPIETRI, Luciano A.; TEODORO, Beatriz; SANTIAGO, Caio R. N.; OLIVEIRA, Guilherme A.; ARAUJO, Jonatas C..
Um sistema de informação extensível para o reconhecimento automático de LIBRAS. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 8. , 2012, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 252-263.
DOI: https://doi.org/10.5753/sbsi.2012.14410.
