On-premise signs detection and recognition using fully convolutional networks

Yong Xiang Wang, Chih Hsin Hsueh, Hung Yi Loo, Min-Chun Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Convolutional neural network has been recently studied and used in many object recognition tasks. In this work, we employ fully convolutional networks (FCNs) to recognize On-Premise Signs (OPS) in real scene. This technology is capable of being utilized in many camera-enabled devices like smart phones to develop practical commercial applications. The fully convolutional network technique is used to train a model to infer whether a street view image contains a specific OPS and where the OPS locates in the input image. Furthermore, to improve the detection performance, data augmentation approaches are applied in our work, and the experiment results show our model outperforms the previous tasks.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Multimedia and Expo, ICME 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781467372589
DOIs
Publication statusPublished - 2016 Aug 25
Event2016 IEEE International Conference on Multimedia and Expo, ICME 2016 - Seattle, United States
Duration: 2016 Jul 112016 Jul 15

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2016-August
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Other

Other2016 IEEE International Conference on Multimedia and Expo, ICME 2016
CountryUnited States
CitySeattle
Period16-07-1116-07-15

Fingerprint

Object recognition
Cameras
Neural networks
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Wang, Y. X., Hsueh, C. H., Loo, H. Y., & Hu, M-C. (2016). On-premise signs detection and recognition using fully convolutional networks. In 2016 IEEE International Conference on Multimedia and Expo, ICME 2016 [7552923] (Proceedings - IEEE International Conference on Multimedia and Expo; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ICME.2016.7552923
Wang, Yong Xiang ; Hsueh, Chih Hsin ; Loo, Hung Yi ; Hu, Min-Chun. / On-premise signs detection and recognition using fully convolutional networks. 2016 IEEE International Conference on Multimedia and Expo, ICME 2016. IEEE Computer Society, 2016. (Proceedings - IEEE International Conference on Multimedia and Expo).
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abstract = "Convolutional neural network has been recently studied and used in many object recognition tasks. In this work, we employ fully convolutional networks (FCNs) to recognize On-Premise Signs (OPS) in real scene. This technology is capable of being utilized in many camera-enabled devices like smart phones to develop practical commercial applications. The fully convolutional network technique is used to train a model to infer whether a street view image contains a specific OPS and where the OPS locates in the input image. Furthermore, to improve the detection performance, data augmentation approaches are applied in our work, and the experiment results show our model outperforms the previous tasks.",
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Wang, YX, Hsueh, CH, Loo, HY & Hu, M-C 2016, On-premise signs detection and recognition using fully convolutional networks. in 2016 IEEE International Conference on Multimedia and Expo, ICME 2016., 7552923, Proceedings - IEEE International Conference on Multimedia and Expo, vol. 2016-August, IEEE Computer Society, 2016 IEEE International Conference on Multimedia and Expo, ICME 2016, Seattle, United States, 16-07-11. https://doi.org/10.1109/ICME.2016.7552923

On-premise signs detection and recognition using fully convolutional networks. / Wang, Yong Xiang; Hsueh, Chih Hsin; Loo, Hung Yi; Hu, Min-Chun.

2016 IEEE International Conference on Multimedia and Expo, ICME 2016. IEEE Computer Society, 2016. 7552923 (Proceedings - IEEE International Conference on Multimedia and Expo; Vol. 2016-August).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Convolutional neural network has been recently studied and used in many object recognition tasks. In this work, we employ fully convolutional networks (FCNs) to recognize On-Premise Signs (OPS) in real scene. This technology is capable of being utilized in many camera-enabled devices like smart phones to develop practical commercial applications. The fully convolutional network technique is used to train a model to infer whether a street view image contains a specific OPS and where the OPS locates in the input image. Furthermore, to improve the detection performance, data augmentation approaches are applied in our work, and the experiment results show our model outperforms the previous tasks.

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Wang YX, Hsueh CH, Loo HY, Hu M-C. On-premise signs detection and recognition using fully convolutional networks. In 2016 IEEE International Conference on Multimedia and Expo, ICME 2016. IEEE Computer Society. 2016. 7552923. (Proceedings - IEEE International Conference on Multimedia and Expo). https://doi.org/10.1109/ICME.2016.7552923