Junfeng Jing

1.9k total citations · 1 hit paper
63 papers, 1.3k citations indexed

About

Junfeng Jing is a scholar working on Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Junfeng Jing has authored 63 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Industrial and Manufacturing Engineering, 42 papers in Computer Vision and Pattern Recognition and 24 papers in Media Technology. Recurrent topics in Junfeng Jing's work include Industrial Vision Systems and Defect Detection (44 papers), Image Processing Techniques and Applications (22 papers) and Image and Object Detection Techniques (15 papers). Junfeng Jing is often cited by papers focused on Industrial Vision Systems and Defect Detection (44 papers), Image Processing Techniques and Applications (22 papers) and Image and Object Detection Techniques (15 papers). Junfeng Jing collaborates with scholars based in China, Australia and South Korea. Junfeng Jing's co-authors include Huanhuan Zhang, Zhen Wang, Matthias Rätsch, Pengfei Li, Weichuan Zhang, Hao Ma, Changming Sun, Gang Wang, Kaibing Zhang and Min Zheng and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Junfeng Jing

60 papers receiving 1.2k citations

Hit Papers

align trajectories

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.

Recent advances on image edge detection: A comprehensive review

2022 142 citations Junfeng Jing et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Junfeng Jing China	18	852	667	358	250	190	63	1.3k
L. Ganesan India	17	134 0.2×	937 1.4×	434 1.2×	55 0.2×	18 0.1×	56	1.4k
Fernando Puente León Germany	17	196 0.2×	373 0.6×	121 0.3×	141 0.6×	7 0.0×	167	1.2k
Wei Gao China	21	42 0.0×	663 1.0×	179 0.5×	73 0.3×	41 0.2×	164	1.4k
Min Tang China	21	54 0.1×	574 0.9×	135 0.4×	406 1.6×	40 0.2×	122	1.6k
Nathan Carr United States	24	128 0.2×	950 1.4×	49 0.1×	820 3.3×	13 0.1×	54	1.8k
Peng Song China	20	179 0.2×	414 0.6×	54 0.2×	429 1.7×	14 0.1×	85	1.4k
Tomislav Pribanić Croatia	13	67 0.1×	941 1.4×	216 0.6×	161 0.6×	5 0.0×	53	1.2k
Yaqin Zhao China	20	39 0.0×	270 0.4×	151 0.4×	52 0.2×	20 0.1×	93	1.0k

Countries citing papers authored by Junfeng Jing

Since Specialization

Citations

This map shows the geographic impact of Junfeng Jing'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 Junfeng Jing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junfeng Jing more than expected).

Fields of papers citing papers by Junfeng Jing

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Junfeng Jing. 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 Junfeng Jing. The network helps show where Junfeng Jing may publish in the future.

Co-authorship network of co-authors of Junfeng Jing

This figure shows the co-authorship network connecting the top 25 collaborators of Junfeng Jing. A scholar is included among the top collaborators of Junfeng Jing 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 Junfeng Jing. Junfeng Jing is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wang, Yuanbo, et al.. (2025). Image anomaly detection with a unified transformer model guided by dual-feature. Journal of Industrial Information Integration. 47. 100892–100892.

Zhang, Huanhuan, et al.. (2025). PUNet: Printed Fabric Defect Segmentation Method Based on UNet. IEEE Transactions on Instrumentation and Measurement. 74. 1–12.

Zhang, Huanhuan, et al.. (2024). Curve-Like Structure Detection Using Multiscale and Boundary-Assisted Segmentation Network. IEEE Transactions on Instrumentation and Measurement. 73. 1–15. 5 indexed citations

Jing, Junfeng, et al.. (2024). Cosine similarity knowledge distillation for surface anomaly detection. Scientific Reports. 14(1). 8150–8150. 4 indexed citations

Jing, Junfeng, et al.. (2023). EDSV-Net: An efficient defect segmentation network based on visual attention and visual perception. Expert Systems with Applications. 237. 121529–121529. 1 indexed citations

Jing, Junfeng, et al.. (2023). An exclusive U-net for fine and crisp edge detection. Multimedia Tools and Applications. 83(18). 54657–54672. 2 indexed citations

Jing, Junfeng & Huaqing Wang. (2023). Defect segmentation with local embedding in industrial 3D point clouds based on transformer. Measurement Science and Technology. 35(3). 35406–35406. 2 indexed citations

Jing, Junfeng, et al.. (2023). MÆIDM: multi-scale anomaly embedding inpainting and discrimination for surface anomaly detection. Machine Vision and Applications. 34(4). 7 indexed citations

Huang, De-Shuang, et al.. (2022). Intelligent Computing Theories and Application. Lecture notes in computer science. 2 indexed citations

10.

Jing, Junfeng, et al.. (2021). EfficientDet for fabric defect detection based on edge computing. Journal of Engineered Fibers and Fabrics. 16. 32 indexed citations

11.

Jing, Junfeng, Zhen Wang, Matthias Rätsch, & Huanhuan Zhang. (2020). Mobile-Unet: An efficient convolutional neural network for fabric defect detection. Textile Research Journal. 92(1-2). 30–42. 206 indexed citations

12.

Jing, Junfeng, et al.. (2020). Fabric defect detection using the improved YOLOv3 model. Journal of Engineered Fibers and Fabrics. 15. 93 indexed citations

13.

Jing, Junfeng, et al.. (2020). A coarse-to-fine method for glass fiber fabric surface defect detection. Journal of the Textile Institute. 112(3). 388–397. 5 indexed citations

14.

Zhang, Huanhuan, et al.. (2019). Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means. Applied Sciences. 9(17). 3506–3506. 26 indexed citations

15.

Zhang, Kaibing, et al.. (2019). Learning a Cascade Regression for No-Reference Super-Resolution Image Quality Assessment. 9273. 450–453. 3 indexed citations

16.

Zhang, Kaibing, et al.. (2018). Fabric Defect Detection Using Salience Metric for Color Dissimilarity and Positional Aggregation. IEEE Access. 6. 49170–49181. 50 indexed citations

17.

Jing, Junfeng, et al.. (2017). Yarn-dyed fabric defect classification based on convolutional neural network. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10420. 104202T–104202T. 6 indexed citations

18.

Li, Ren-Zhong, et al.. (2015). Fabric defect detection based on wavelet transform and k-means. Advances in computer science research. 24. 2 indexed citations

19.

Jing, Junfeng, et al.. (2011). Combined fabric defects detection approach and quadtree decomposition. Journal of Industrial Textiles. 41(4). 331–344. 8 indexed citations

20.

Jing, Junfeng, et al.. (2011). Automatic Classification of Woven Fabric Structure by Using Learning Vector Quantization. Procedia Engineering. 15. 5005–5009. 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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