| A deep normalization and convolutional neural network for image smoke detection Z Yin, B Wan, F Yuan, X Xia, J Shi Ieee Access 5, 18429-18438, 2017 | 295 | 2017 |
| Weakly supervised video anomaly detection via center-guided discriminative learning B Wan, Y Fang, X Xia, J Mei 2020 IEEE international conference on multimedia and expo (ICME), 1-6, 2020 | 168 | 2020 |
| Deep smoke segmentation F Yuan, L Zhang, X Xia, B Wan, Q Huang, X Li Neurocomputing 357, 248-260, 2019 | 145 | 2019 |
| High-order local ternary patterns with locality preserving projection for smoke detection and image classification F Yuan, J Shi, X Xia, Y Fang, Z Fang, T Mei Information Sciences 372, 225-240, 2016 | 116 | 2016 |
| A wave-shaped deep neural network for smoke density estimation F Yuan, L Zhang, X Xia, Q Huang, X Li IEEE transactions on image processing 29, 2301-2313, 2019 | 74 | 2019 |
| A gated recurrent network with dual classification assistance for smoke semantic segmentation F Yuan, L Zhang, X Xia, Q Huang, X Li IEEE Transactions on Image Processing 30, 4409-4422, 2021 | 73 | 2021 |
| Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition F Yuan, L Zhang, B Wan, X Xia, J Shi Machine Vision and Applications 30, 345-358, 2019 | 69 | 2019 |
| Sub oriented histograms of local binary patterns for smoke detection and texture classification F Yuan, J Shi, X Xia, Y Yang, Y Fang, R Wang KSII Transactions on Internet and Information Systems (TIIS) 10 (4), 1807-1823, 2016 | 47 | 2016 |
| Non-linear dimensionality reduction and Gaussian process based classification method for smoke detection F Yuan, X Xia, J Shi, H Li, G Li IEEE Access 5, 6833-6841, 2017 | 46 | 2017 |
| Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns F Yuan, X Xia, J Shi Information Sciences 460, 202-222, 2018 | 29 | 2018 |
| Video smoke detection: a literature survey J Shi, F Yuan, X Xia Image Graph 23 (3), 303-322, 2018 | 29 | 2018 |
| Encoding pairwise Hamming distances of Local Binary Patterns for visual smoke recognition F Yuan, J Shi, X Xia, L Zhang, S Li Computer Vision and Image Understanding 178, 43-53, 2019 | 27 | 2019 |
| Cubic-cross convolutional attention and count prior embedding for smoke segmentation F Yuan, Z Dong, L Zhang, X Xia, J Shi Pattern Recognition 131, 108902, 2022 | 21 | 2022 |
| Image dehazing based on a transmission fusion strategy by automatic image matting F Yuan, Y Zhou, X Xia, J Shi, Y Fang, X Qian Computer Vision and Image Understanding 194, 102933, 2020 | 21 | 2020 |
| A confidence prior for image dehazing F Yuan, Y Zhou, X Xia, X Qian, J Huang Pattern Recognition 119, 108076, 2021 | 20 | 2021 |
| From traditional methods to deep ones: review of visual smoke recognition, detection, and segmentation X Xia, FN Yuan, L Zhang, LZ Yang, JT Shi Journal of Image and Graphics 24 (10), 1627-1647, 2019 | 20 | 2019 |
| Fusing texture, edge and line features for smoke recognition F Yuan, G Li, X Xia, B Lei, J Shi IET Image Processing 13 (14), 2805-2812, 2019 | 16 | 2019 |
| Learning multi-scale and multi-order features from 3D local differences for visual smoke recognition F Yuan, X Xia, J Shi, L Zhang, J Huang Information Sciences 468, 193-212, 2018 | 16 | 2018 |
| Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition F Yuan, J Shi, X Xia, Q Huang, X Li IET Computer Vision 13 (2), 178-187, 2019 | 10 | 2019 |
| A review of the theory and application of self coding neural networks [J] Y Feiniu, Z Lin, S Jinting, X Xue, L Gang Acta Sinica Sinica 42 (001), 203-230, 2019 | 10 | 2019 |
