A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system

Zourong Long; Biao Wei; Peng Feng; Pengwei Yu; Yuanyuan Liu

doi:10.1117/12.2295529

12 January 2018 A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system

Zourong Long, Biao Wei, Peng Feng, Pengwei Yu, Yuanyuan Liu

Proceedings Volume 10621, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems; 106211W (2018) https://doi.org/10.1117/12.2295529
Event: International Conference on Optical Instruments and Technology 2017, 2017, Beijing, China

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.

Citation Download Citation

Zourong Long, Biao Wei, Peng Feng, Pengwei Yu, and Yuanyuan Liu "A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system", Proc. SPIE 10621, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 106211W (12 January 2018); https://doi.org/10.1117/12.2295529

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