Jun Sang

1.7k total citations · 1 hit paper
73 papers, 1.2k citations indexed

About

Jun Sang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Jun Sang has authored 73 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Computer Vision and Pattern Recognition, 23 papers in Artificial Intelligence and 12 papers in Information Systems. Recurrent topics in Jun Sang's work include Advanced Steganography and Watermarking Techniques (17 papers), Video Surveillance and Tracking Methods (17 papers) and Chaos-based Image/Signal Encryption (17 papers). Jun Sang is often cited by papers focused on Advanced Steganography and Watermarking Techniques (17 papers), Video Surveillance and Tracking Methods (17 papers) and Chaos-based Image/Signal Encryption (17 papers). Jun Sang collaborates with scholars based in China, United States and Pakistan. Jun Sang's co-authors include Haibo Hu, Mohammad S. Alam, Hong Xiang, Bin Cai, Nasrullah Nasrullah, Muhammad Azeem Akbar, Muhammad Mateen, Arif Ali Khan, Zhongyuan Wu and Qian Zhang and has published in prestigious journals such as Polymer, IEEE Access and Sensors.

In The Last Decade

Jun Sang

59 papers receiving 1.1k citations

Hit Papers

Automated Lung Nodule Detection and Classification Using ... 2019 2026 2021 2023 2019 50 100 150 200
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. Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies
    2019 236 citations Jun Sang, Mohammad S. Alam et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jun Sang China 18 452 302 269 253 166 73 1.2k
Rahul Kumar Singh India 14 376 0.8× 520 1.7× 128 0.5× 687 2.7× 189 1.1× 56 1.4k
Mukesh Soni India 22 174 0.4× 472 1.6× 324 1.2× 220 0.9× 36 0.2× 114 1.4k
Dheyaa Ahmed Ibrahim Iraq 21 502 1.1× 577 1.9× 152 0.6× 316 1.2× 32 0.2× 37 1.4k
Md Whaiduzzaman Bangladesh 20 171 0.4× 462 1.5× 670 2.5× 102 0.4× 42 0.3× 61 1.8k
Hossam Hawash Egypt 18 237 0.5× 674 2.2× 159 0.6× 142 0.6× 34 0.2× 40 1.3k
Maria Ganzha Poland 13 132 0.3× 327 1.1× 245 0.9× 65 0.3× 15 0.1× 124 922
Shu Zhao China 20 275 0.6× 649 2.1× 306 1.1× 115 0.5× 23 0.1× 110 1.1k
S. K. Lakshmanaprabu India 16 269 0.6× 314 1.0× 237 0.9× 236 0.9× 9 0.1× 33 1.1k
Mohammad Ayoub Khan Saudi Arabia 20 129 0.3× 337 1.1× 394 1.5× 117 0.5× 18 0.1× 67 1.5k
Haris Pervaiz United Kingdom 25 170 0.4× 293 1.0× 343 1.3× 144 0.6× 57 0.3× 116 2.3k

Countries citing papers authored by Jun Sang

Since Specialization
Citations

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

Fields of papers citing papers by Jun Sang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Sang

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Sang. A scholar is included among the top collaborators of Jun Sang 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 Jun Sang. Jun Sang 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.
Long, Fei, et al.. (2025). Multi-scale Vision Mamba-UNet: A Mamba-based method for retinal vessel segmentation. Biomedical Signal Processing and Control. 112. 108435–108435.
2.
Cao, Jian, et al.. (2025). PolaCount: Text-specified zero-shot object counting with pyramid polarity-aware cross-attention. Neurocomputing. 652. 131135–131135.
3.
Xia, Xiaofeng, et al.. (2025). Cross-Level Attention Multi-Scale Context-Enhanced Crowd Counting Network for Transportation Cyber-Physical Systems. IEEE Transactions on Intelligent Transportation Systems. 26(9). 14250–14263.
4.
Long, Fei, et al.. (2025). A diabetic retinopathy classification method based on image-text contrastive learning. Pattern Recognition Letters. 193. 142–148. 1 indexed citations
5.
Wei, Ning, et al.. (2025). Weakly supervised crowd counting with joint CNN and transformer network. Pattern Recognition. 171. 112077–112077.
6.
Sang, Jun, et al.. (2025). CrossVesselNet: A one-shot retinal vessel segmentation Net. Biomedical Signal Processing and Control. 113. 108857–108857.
7.
8.
Liu, Kai, et al.. (2025). Modal-Adaptive Spatial-Aware-Fusion and Propagation Network for Multimodal Vision Crowd Counting. IEEE Transactions on Consumer Electronics. 71(2). 3605–3616.
9.
Long, Fei, et al.. (2024). Multi-GlaucNet: A multi-task model for optic disc segmentation, blood vessel segmentation and glaucoma detection. Biomedical Signal Processing and Control. 99. 106850–106850. 2 indexed citations
10.
Ma, Rui, et al.. (2024). NFGCL: A negative-sampling-free graph contrastive learning framework for recommendation. Information Sciences. 695. 121732–121732. 1 indexed citations
11.
12.
Gong, Wenhui, et al.. (2023). Makeup transfer based on Laplacian pyramid network. 27–27. 1 indexed citations
13.
Liu, Xinyue, et al.. (2021). Density-aware and background-aware network for crowd counting via multi-task learning. Pattern Recognition Letters. 150. 221–227. 10 indexed citations
14.
Iqbal, Nayyar & Jun Sang. (2021). Fuzzy Logic Testing Approach for Measuring Software Completeness. Symmetry. 13(4). 604–604. 5 indexed citations
15.
Long, Fei, Jing-Jie Peng, Weitao Song, Xiaobo Xia, & Jun Sang. (2021). BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells. Computer Methods and Programs in Biomedicine. 202. 105972–105972. 39 indexed citations
16.
Iqbal, Nayyar, Jun Sang, Min Gao, Haibo Hu, & Hong Xiang. (2019). Forward Engineering Completeness for Software by Using Requirements Validation Framework (S). Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering. 2019. 523–528. 2 indexed citations
17.
Xu, Xiaoying, Grau Baquero, Rita Puig, et al.. (2015). Carbon Footprint and Toxicity Indicators of Alternative Chromium Free Tanning in China. Journal of the American Leather Chemists Association. 110(5). 130–137. 7 indexed citations
18.
Naeem, Hamad, et al.. (2015). Message Encryption by Processing Image Using Pseudo Random Key Streams Generation. International Journal of Computer Trends and Technology. 20(2). 78–82. 1 indexed citations
19.
Xu, Hongsheng, Nong Sang, Bing Zhang, & Jun Sang. (2014). Attack on double-random-phase-encoding-based image hiding method. Optik. 125(13). 3043–3050. 1 indexed citations
20.
Sang, Jun, et al.. (2012). High Capacity Steganographic Scheme for JPEG Compression Using Particle Swarm Optimization. Advanced materials research. 433-440. 5118–5122. 2 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Rahul Kumar Singh Breakdown of academic impact, for papers by Mukesh Soni Breakdown of academic impact, for papers by Dheyaa Ahmed Ibrahim Breakdown of academic impact, for papers by Md Whaiduzzaman Breakdown of academic impact, for papers by Hossam Hawash Breakdown of academic impact, for papers by Maria Ganzha Breakdown of academic impact, for papers by Shu Zhao Breakdown of academic impact, for papers by S. K. Lakshmanaprabu Breakdown of academic impact, for papers by Mohammad Ayoub Khan Breakdown of academic impact, for papers by Haris Pervaiz
Rankless by CCL
2026