King‐Sun Fu

88 papers receiving 4.5k citations

Hit Papers

Shape Discrimination Using Fourier Descriptors197720261993200919771983200400600

Peers

King‐Sun Fu
Comparison fields: 5 of 165
  • Computer Vision and Pattern Recognition 2.6k
  • Artificial Intelligence 1.8k
  • Signal Processing 842
  • Computational Theory and Mathematics 562
  • Computer Networks and Communications 502
Replace Pasquale Foggia with:
Pasquale Foggia Italy
Jack Sklansky United States
K. S. Fu United States
David A. Huffman United States
Raphael A. Finkel United States
Solomon W. Golomb United States
Petros Drineas United States
John Moody United States
E. N. Gilbert United States
M.N.S. Swamy Canada
King‐Sun Fu relative to Pasquale Foggia Italy Pasquale Foggia's profile →
Citations per field
00.5×1.5×
Pasquale Foggia · 1×
Citations per year

Countries citing papers authored by King‐Sun Fu

Since Specialization
Citations

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

Fields of papers citing papers by King‐Sun Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by King‐Sun Fu. 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 King‐Sun Fu. The network helps show where King‐Sun Fu may publish in the future.

Co-authorship network of co-authors of King‐Sun Fu

This figure shows the co-authorship network connecting the top 25 collaborators of King‐Sun Fu. A scholar is included among the top collaborators of King‐Sun Fu 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 King‐Sun Fu. King‐Sun Fu 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
#WorkIndexed citations
1 6
2 3
3 35
4 60
5 40
6 5
7
BRANCH AND BOUND SEARCH FOR AUTOMATIC LINKING PROCESS OF SEISMOGRAM.
1
8 38
9
Matching parallel algorithm and architecture
2
10 30
11
A CSG Based DBMS for CAD/CAM and its Supporting Query Language.
14
12 11
13 463
14 78
15
Pattern Recognition and Damage Assessment
5
16 5
17
Shape Discrimination Using Fourier Descriptorsbreakdown →
743
18 66
19 2
20 0

About King‐Sun Fu

King‐Sun Fu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Hardware and Architecture, having authored 94 papers that have together received 4.9k indexed citations. Recurring topics across this work include Algorithms and Data Compression (20 papers), Machine Learning and Algorithms (10 papers) and Image Retrieval and Classification Techniques (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.6k citations), Signal Processing (842 citations) and Artificial Intelligence (1.8k citations). King‐Sun Fu has collaborated with scholars based in United States, Taiwan and Netherlands. Frequent co-authors include E. Persoon, Alberto Sanfeliu, Wen‐Hsiang Tsai, М.А. Айзерман, Taylor L. Booth, Thomas S. Huang, Manfred R. Schroeder, Henri J. Nussbaumer, M. A. Eshera and Ning-San Chang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Automatic Control and IEEE Transactions on Information Theory.

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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