Feiniu Yuan

3.0k total citations · 1 hit paper
67 papers, 2.2k citations indexed

About

Feiniu Yuan is a scholar working on Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality and Artificial Intelligence. According to data from OpenAlex, Feiniu Yuan has authored 67 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Computer Vision and Pattern Recognition, 34 papers in Safety, Risk, Reliability and Quality and 7 papers in Artificial Intelligence. Recurrent topics in Feiniu Yuan's work include Fire Detection and Safety Systems (34 papers), Video Surveillance and Tracking Methods (33 papers) and Image Enhancement Techniques (28 papers). Feiniu Yuan is often cited by papers focused on Fire Detection and Safety Systems (34 papers), Video Surveillance and Tracking Methods (33 papers) and Image Enhancement Techniques (28 papers). Feiniu Yuan collaborates with scholars based in China, Singapore and United Kingdom. Feiniu Yuan's co-authors include Xue Xia, Zhijun Fang, Jinting Shi, Zhengxiao Zhang, Qinghua Huang, Xuelong Li, Boyang Wan, Yuming Fang, Zhijian Yin and Lin Zhang and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Feiniu Yuan

62 papers receiving 2.2k citations

Hit Papers

An effective CNN and Transformer complementary network fo... 2022 2026 2023 2024 2022 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. An effective CNN and Transformer complementary network for medical image segmentation
    2022 306 citations Feiniu Yuan, Zhijun Fang et al. Pattern Recognition profile →

Peers

Feiniu Yuan
Qiao Liu China
Tingfa Xu China
Di Yuan China
Peng Chu China
Liejun Wang China
Vishwanath A. Sindagi United States
Behçet Uğur Töreyın Türkiye
Suha Kwak South Korea
Qiao Liu China
Feiniu Yuan
Citations per year, relative to Feiniu Yuan Feiniu Yuan (= 1×) peers Qiao Liu

Countries citing papers authored by Feiniu Yuan

Since Specialization
Citations

This map shows the geographic impact of Feiniu Yuan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Feiniu Yuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feiniu Yuan more than expected).

Fields of papers citing papers by Feiniu Yuan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Feiniu Yuan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Feiniu Yuan. The network helps show where Feiniu Yuan may publish in the future.

Co-authorship network of co-authors of Feiniu Yuan

This figure shows the co-authorship network connecting the top 25 collaborators of Feiniu Yuan. A scholar is included among the top collaborators of Feiniu Yuan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Feiniu Yuan. Feiniu Yuan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Xiangfen, et al.. (2025). MOSSNet: A Lightweight Dual-Branch Multiscale Attention Neural Network for Bryophyte Identification. Symmetry. 17(3). 347–347. 3 indexed citations
2.
Zhang, Xiangfen, et al.. (2025). A dual-level part distillation network for fine-grained visual categorization. Signal Processing Image Communication. 138. 117383–117383.
3.
Zhang, Xiangfen, et al.. (2024). MMAformer: Multiscale Modality-Aware Transformer for Medical Image Segmentation. Electronics. 13(23). 4636–4636. 2 indexed citations
4.
Ma, Yan, et al.. (2024). Dual-Guided Frequency Prototype Network for Few-Shot Semantic Segmentation. IEEE Transactions on Multimedia. 26. 8874–8888. 3 indexed citations
5.
Yuan, Feiniu, Y. Peng, Qinghua Huang, & Xuelong Li. (2024). A Bi-Directionally Fused Boundary Aware Network for Skin Lesion Segmentation. IEEE Transactions on Image Processing. 33. 6340–6353. 1 indexed citations
6.
Yuan, Feiniu, et al.. (2024). A newton interpolation network for smoke semantic segmentation. Pattern Recognition. 159. 111119–111119. 3 indexed citations
7.
Yuan, Feiniu, Yu Shi, Lin Zhang, & Yuming Fang. (2023). A cross-scale mixed attention network for smoke segmentation. Digital Signal Processing. 134. 103924–103924. 9 indexed citations
8.
Yuan, Feiniu, et al.. (2023). Edge-reinforced attention network for smoke semantic segmentation. Multimedia Tools and Applications. 82(20). 31259–31284. 4 indexed citations
9.
Zhou, Fangrong, et al.. (2023). A boundary enhancement and pixel alignment based smoke segmentation network. Frontiers in Physics. 11. 1 indexed citations
10.
Zhou, Fangrong, et al.. (2023). A dense multi‐scale context and asymmetric pooling embedding network for smoke segmentation. IET Computer Vision. 18(2). 236–246. 5 indexed citations
11.
Yuan, Feiniu, et al.. (2023). A multiple gated boosting network for multi‐organ medical image segmentation. IET Image Processing. 17(10). 3028–3039. 4 indexed citations
12.
Yuan, Feiniu, Kang Li, Chunmei Wang, & Zhijun Fang. (2023). A lightweight network for smoke semantic segmentation. Pattern Recognition. 137. 109289–109289. 26 indexed citations
13.
Yuan, Feiniu, Lin Zhang, & Xue Xia. (2023). Smoke semantic segmentation with multi-scale residual paths and weighted middle surveillances. Multimedia Tools and Applications. 83(16). 47199–47224. 6 indexed citations
14.
Yuan, Feiniu, Lin Zhang, Xue Xia, Qinghua Huang, & Xuelong Li. (2021). A Gated Recurrent Network With Dual Classification Assistance for Smoke Semantic Segmentation. IEEE Transactions on Image Processing. 30. 4409–4422. 77 indexed citations
15.
Yuan, Feiniu, Yu Zhou, Xue Xia, et al.. (2020). Image dehazing based on a transmission fusion strategy by automatic image matting. Computer Vision and Image Understanding. 194. 102933–102933. 16 indexed citations
16.
Huang, Qinghua, et al.. (2020). Segmentation of breast ultrasound image with semantic classification of superpixels. Medical Image Analysis. 61. 101657–101657. 167 indexed citations
17.
Yuan, Feiniu, et al.. (2019). A Wave-Shaped Deep Neural Network for Smoke Density Estimation. IEEE Transactions on Image Processing. 29. 2301–2313. 77 indexed citations
18.
Fang, Yuming, Xiaoqiang Zhang, Feiniu Yuan, Nevrez İmamoğlu, & Haiwen Liu. (2019). Video saliency detection by gestalt theory. Pattern Recognition. 96. 106987–106987. 18 indexed citations
19.
Yuan, Feiniu. (2012). An Interactive Concave Volume Clipping Method Based on GPU Ray Casting with Boolean Operation. Computing and Informatics / Computers and Artificial Intelligence. 31(3). 551. 1 indexed citations
20.
Yuan, Feiniu, et al.. (2011). Modeling n-Furcated Liver vessels From a 3-D Segmented Volume Using Hole-Making and Subdivision Methods. IEEE Transactions on Biomedical Engineering. 59(2). 552–561. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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