The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Des recherches récentes sur les robots mobiles montrent que les réseaux neuronaux convolutifs (CNN) ont atteint des performances impressionnantes en matière de reconnaissance visuelle de lieux, en particulier dans les environnements dynamiques à grande échelle. Cependant, CNN conduit à un vaste espace de représentation d’images qui ne peut pas répondre à la demande en temps réel de navigation robotisée. Visant ce problème, nous évaluons l'efficacité des caractéristiques des cartes de caractéristiques obtenues à partir de la couche de CNN par variance et proposons une nouvelle méthode qui réserve les cartes de caractéristiques saillantes et effectue une binarisation adaptative pour celles-ci. Les résultats expérimentaux démontrent l’efficacité et l’efficience de notre méthode. Par rapport aux méthodes de reconnaissance visuelle de lieux de pointe, notre méthode ne présente non seulement aucune perte significative de précision, mais réduit également considérablement l’espace de représentation de l’image.
Yutian CHEN
Army Engineering University of PLA
Wenyan GAN
Army Engineering University of PLA
Shanshan JIAO
Army Engineering University of PLA
Youwei XU
Army Engineering University of PLA
Yuntian FENG
Army Engineering University of PLA
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Yutian CHEN, Wenyan GAN, Shanshan JIAO, Youwei XU, Yuntian FENG, "Salient Feature Selection for CNN-Based Visual Place Recognition" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 3102-3107, December 2018, doi: 10.1587/transinf.2018EDP7175.
Abstract: Recent researches on mobile robots show that convolutional neural network (CNN) has achieved impressive performance in visual place recognition especially for large-scale dynamic environment. However, CNN leads to the large space of image representation that cannot meet the real-time demand for robot navigation. Aiming at this problem, we evaluate the feature effectiveness of feature maps obtained from the layer of CNN by variance and propose a novel method that reserve salient feature maps and make adaptive binarization for them. Experimental results demonstrate the effectiveness and efficiency of our method. Compared with state of the art methods for visual place recognition, our method not only has no significant loss in precision, but also greatly reduces the space of image representation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7175/_p
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@ARTICLE{e101-d_12_3102,
author={Yutian CHEN, Wenyan GAN, Shanshan JIAO, Youwei XU, Yuntian FENG, },
journal={IEICE TRANSACTIONS on Information},
title={Salient Feature Selection for CNN-Based Visual Place Recognition},
year={2018},
volume={E101-D},
number={12},
pages={3102-3107},
abstract={Recent researches on mobile robots show that convolutional neural network (CNN) has achieved impressive performance in visual place recognition especially for large-scale dynamic environment. However, CNN leads to the large space of image representation that cannot meet the real-time demand for robot navigation. Aiming at this problem, we evaluate the feature effectiveness of feature maps obtained from the layer of CNN by variance and propose a novel method that reserve salient feature maps and make adaptive binarization for them. Experimental results demonstrate the effectiveness and efficiency of our method. Compared with state of the art methods for visual place recognition, our method not only has no significant loss in precision, but also greatly reduces the space of image representation.},
keywords={},
doi={10.1587/transinf.2018EDP7175},
ISSN={1745-1361},
month={December},}
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TY - JOUR
TI - Salient Feature Selection for CNN-Based Visual Place Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 3102
EP - 3107
AU - Yutian CHEN
AU - Wenyan GAN
AU - Shanshan JIAO
AU - Youwei XU
AU - Yuntian FENG
PY - 2018
DO - 10.1587/transinf.2018EDP7175
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - Recent researches on mobile robots show that convolutional neural network (CNN) has achieved impressive performance in visual place recognition especially for large-scale dynamic environment. However, CNN leads to the large space of image representation that cannot meet the real-time demand for robot navigation. Aiming at this problem, we evaluate the feature effectiveness of feature maps obtained from the layer of CNN by variance and propose a novel method that reserve salient feature maps and make adaptive binarization for them. Experimental results demonstrate the effectiveness and efficiency of our method. Compared with state of the art methods for visual place recognition, our method not only has no significant loss in precision, but also greatly reduces the space of image representation.
ER -