Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141004(2020)
A Neural Network for Multi-Spectral Semantic Segmentation Based on LBP Feature Enhancement
In order to improve the accuracy of multi-spectral image semantic segmentation, a neural network model based on local binary pattern (LBP) feature enhancement is proposed. The model obtains two feature maps from a single infrared image by two LBP feature extraction operators with the size of 3×3 and 5×5, respectively. The RGB image, the infrared image, and the LBP feature maps are imported into a neural network model with a 34-layer residual network for semantic segmentation. The experimental results show that the proposed neural network model can achieve an average accuracy of 60.7% and an average intersection over union of 51.9% on the RGB-Thermal dataset. The results are superior to other comparative methods. At the same time, in the visualization results, the results of proposed model are also more clear and accurate.
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Xingping Shi, Jiangtao Xu, Yongtang Jiang, Shuzhen Qin, Kaige Lu. A Neural Network for Multi-Spectral Semantic Segmentation Based on LBP Feature Enhancement[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141004
Category: Image Processing
Received: Oct. 18, 2019
Accepted: Nov. 26, 2019
Published Online: Jul. 24, 2020
The Author Email: Qin Shuzhen (brightmoonqsz@tju.edu.cn)