John See

4.8k citations
121 papers · 2.4k indexed · h-index 27

John See

110 papers receiving 2.3k citations

Peers

John See
Comparison fields: 5 of 113
  • Computer Vision and Pattern Recognition 1.7k
  • Experimental and Cognitive Psychology 1.0k
  • Human-Computer Interaction 325
  • Urban Studies 213
  • Signal Processing 264
Replace Xiaojiang Peng with:
Xiaojiang Peng China
Tomas Pfister United States
Irene Kotsia United Kingdom
Thiago Oliveira-Santos Brazil
Qirong Mao China
Kang Park South Korea
Jiabei Zeng China
Edilson de Aguiar Germany
Yong Du China
Hailin Shi China
John See relative to Xiaojiang Peng China Xiaojiang Peng's profile →
Citations per field
00.5×1.5×2.5×
Xiaojiang Peng · 1×
Citations per year

Countries citing papers authored by John See

Since Specialization
Citations

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

Fields of papers citing papers by John See

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside John See, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John See Line = papers co-authored together John See links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20241
4 20240
5 20240
6 20235
7 20230
8 20238
9 20232
10 20229
11 20215
12 202041
13
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
20205
14 20192
15 2018119
16 20182
17 201839
18
Human Motion Detection Using Fuzzy Rule-base Classification Of Moving Blob Regions
20151
19 201569
20
Ten Criteria for Effective Technology Plans.
19921

About John See

John See is a scholar working on Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Human-Computer Interaction, Signal Processing and Artificial Intelligence, having authored 121 papers that have together received 2.4k indexed citations. Recurring topics across this work include Emotion and Mood Recognition (28 papers), Face and Expression Recognition (25 papers), Human Pose and Action Recognition (25 papers), Video Surveillance and Tracking Methods (22 papers), Face recognition and analysis (18 papers), Advanced Image and Video Retrieval Techniques (17 papers), Anomaly Detection Techniques and Applications (15 papers) and Advanced Neural Network Applications (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.7k citations), Experimental and Cognitive Psychology (1.0k citations), Human-Computer Interaction (325 citations), Urban Studies (213 citations) and Signal Processing (264 citations). John See has collaborated with scholars based in Malaysia, China and United Kingdom. Frequent co-authors include Raphaël C.‐W. Phan, Sze‐Teng Liong, Weiyao Lin, KokSheik Wong, Anh Cat Le Ngo, Yandan Wang, Huai-Qian Khor, Yee-Hui Oh, Vishnu Monn Baskaran and Wenbin Liu. Their work appears in journals such as Signal Processing Image Communication, Neurocomputing, IEEE Transactions on Image Processing, IEEE Transactions on Multimedia and International Journal of Computer Vision.

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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