Ming Xiang

669 total citations
24 papers, 249 citations indexed

About

Ming Xiang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Ming Xiang has authored 24 papers receiving a total of 249 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 10 papers in Computer Networks and Communications. Recurrent topics in Ming Xiang's work include Distributed Sensor Networks and Detection Algorithms (9 papers), Target Tracking and Data Fusion in Sensor Networks (9 papers) and Face and Expression Recognition (7 papers). Ming Xiang is often cited by papers focused on Distributed Sensor Networks and Detection Algorithms (9 papers), Target Tracking and Data Fusion in Sensor Networks (9 papers) and Face and Expression Recognition (7 papers). Ming Xiang collaborates with scholars based in China. Ming Xiang's co-authors include Yupei Zhang, Bo Yang, Fang Wen, Chongzhao Han, Fangyun Wei, Ting Zhang, Chen Dong, Nanning Zheng, Dong Chen and Jing Li and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and Neurocomputing.

In The Last Decade

Ming Xiang

21 papers receiving 240 citations

Peers

Ming Xiang
Comparison fields: 5 of 53
  • Computer Vision and Pattern Recognition 144
  • Artificial Intelligence 93
  • Computer Networks and Communications 43
  • Computational Mechanics 42
  • Media Technology 36
Dong Yin China
Donghui Wang China
Xianzhong Long China
Huidong Liu China
Chao Lan United States
Limei Zhang China
Wangmeng Zuo China
Yifan Fu Australia
Pavan Kumar Mallapragada United States
Changbo Zhu United States
Dong Yin China View profile →
Citations per field, relative to Ming Xiang
Ming Xiang · 1×
Citations per year, relative to Ming Xiang
Ming Xiang · 1×

Countries citing papers authored by Ming Xiang

Since Specialization
Citations

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

Fields of papers citing papers by Ming Xiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Xiang

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Xiang. A scholar is included among the top collaborators of Ming Xiang 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 Ming Xiang. Ming Xiang 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
# Work Indexed citations
1 2
2 13
3 23
4 6
5 56
6 2
7 21
8 3
9 8
10 3
11
Fingerprint Image Enhancement and Minutiae Extraction
14
12 0
13 1
14 1
15 3
16 2
17
Optimum Detection Fusion Algorithm for Distributed and Quantized Neyman-Pearson Detection Systems
1
18
Detection Fusion Algorithm for Distributed Neyman-Pearson Detection Systems with Serial Structures
0
19 3
20 22

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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