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Proceedings Paper

A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system
Author(s): Zourong Long; Biao Wei; Peng Feng; Pengwei Yu; Yuanyuan Liu
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Paper Abstract

This paper proposes an image segmentation algorithm with fully convolutional networks (FCN) in binocular imaging system under various circumstance. Image segmentation is perfectly solved by semantic segmentation. FCN classifies the pixels, so as to achieve the level of image semantic segmentation. Different from the classical convolutional neural networks (CNN), FCN uses convolution layers instead of the fully connected layers. So it can accept image of arbitrary size. In this paper, we combine the convolutional neural network and scale invariant feature matching to solve the problem of visual positioning under different scenarios. All high-resolution images are captured with our calibrated binocular imaging system and several groups of test data are collected to verify this method. The experimental results show that the binocular images are effectively segmented without over-segmentation. With these segmented images, feature matching via SURF method is implemented to obtain regional information for further image processing. The final positioning procedure shows that the results are acceptable in the range of 1.4∼1.6 m, the distance error is less than 10mm.

Paper Details

Date Published: 12 January 2018
PDF: 8 pages
Proc. SPIE 10621, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 106211W (12 January 2018); doi: 10.1117/12.2295529
Show Author Affiliations
Zourong Long, Chongqing Univ. (China)
Biao Wei, Chongqing Univ. (China)
Peng Feng, Chongqing Univ. (China)
Pengwei Yu, Chongqing Univ. (China)
Yuanyuan Liu, Chongqing Univ. (China)


Published in SPIE Proceedings Vol. 10621:
2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems
Jigui Zhu; Hwa-Yaw Tam; Kexin Xu; Hai Xiao; Liquan Dong, Editor(s)

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