期刊论文

Chen, Yuantao

英文

Journal of Ambient Intelligence and Humanized Computing

1868-5137

2022

13

11

5033-5045

10.1007/s12652-020-02066-z

National Science Foundation of ChinaNational Natural Science Foundation of China [61972056, 61972212, 61402053, 61981340416]; Open Research Fund of Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation [2015TP1005]; Changsha Science and Technology Planning [KQ1703018, KQ1706064, KQ1703018-01, KQ1703018-04]; Research Foundation of Education Bureau of Hunan Province [17A007, 19B005]; Changsha Industrial Science and Technology Commissioner [2017-7]; Junior Faculty Development Program Project of Changsha University of Science and Technology [2019QJCZ011]; Natural Science Foundation of Hunan ProvinceNatural Science Foundation of Hunan Province [2020JJ50590]; Program of Practical Innovation and Entrepreneurship Improvement [CSLG2020]

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The context information of images had been lost due to the low resolution of features, and due to repeated combinations of max-pooling layer and down-sampling layer. When the feature extraction process had been performed using a convolutional network, the result of semantic image segmentation loses sensitivity to the location of the object. The semantic image segmentation based on a feature fusion model with context features layer-by-layer had been proposed. Firstly, the original images had been pre-processed by the Gaussian Kernel Function to generate a series of images with different resolut...