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Author |
A. Martinez; Jordi Vitria; J. Lopez |
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Visual Recognition of Surroundings: A robot that knows where it is. |
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Miscellaneous |
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1997 |
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Intelligence Artificielle et Complexite. |
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Paris. |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ MVL1997 |
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59 |
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Author |
Javier Varona; A. Pujol; Juan J. Villanueva |
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Title |
Visual tracking in application domains. |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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Bilbao |
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ISE @ ise @ VPV1999 |
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10 |
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Author |
Javier Varona; A. Pujol; Juan J. Villanueva |
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Title |
Visual Tracking in Application Domains. |
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Miscellaneous |
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2000 |
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Pattern Recognition and Applications, IOS Press, 99–106. |
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ISE @ ise @ VPV2000 |
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333 |
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Author |
Felipe Lumbreras; Joan Serrat |
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Title |
Wavelet filtering for the segmentation of marble images. |
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1996 |
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Optical Engineering |
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ADAS @ adas @ LuS1996a |
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77 |
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Author |
David Guillamet; M. Bressan; Jordi Vitria |
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Title |
Weighted Non-negative Matrix Factorization for Local Representations. |
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Miscellaneous |
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2001 |
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Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). |
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Hawaii |
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OR;MV |
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BCNPCL @ bcnpcl @ GBV2001 |
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96 |
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Author |
Juan Andrade; F. Thomas |
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Title |
Wire-Based Tracking using Mutual Information |
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Miscellaneous |
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2006 |
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10th International Symposium on Advances in Robot Kinematics, 3–14 |
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Ljubljana (Slovenia) |
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Admin @ si @ AnT2006 |
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665 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Word separation in continuous sign language using isolated signs and post-processing |
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2022 |
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Arxiv |
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Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLVID, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection. |
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HUPBA; no menciona |
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Admin @ si @ RKE2022b |
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3824 |
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