Michael Seibert

942 citations
28 papers · 688 · h-index 13

Impact in

Papers in

Michael Seibert

27 papers receiving 604 citations

Peers

Michael Seibert
Comparison fields: 5 of 71
  • Media Technology 144
  • Computer Vision and Pattern Recognition 289
  • Ocean Engineering 144
  • Artificial Intelligence 229
  • Signal Processing 75
Replace You He with:
You He China
Shinichiro Haruyama Japan
Xiaojun Wu China
Ki-Sang Hong South Korea
Rongxin Jiang China
Yizeng Han China
Zhen Dong China
Rui Lü China
Lambert E. Wixson United States
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Citations per field
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Citations per year

Countries citing papers authored by Michael Seibert

Since Specialization
Citations

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

Fields of papers citing papers by Michael Seibert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 16 scholars most cited alongside Michael Seibert, 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 Michael Seibert Line = papers co-authored together Michael Seibert links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1992118
2 1997100
3 200683
4 200666
5 200448
6 199542
7 199542
8 198934
9 200229
10 200628
11
Learning Aspect Graph Representations from View Sequences
198921
12 200615
13 200712
14 20079
15
Neural Analog Diffusion-Enhancement Layer and Spatio-Temporal Grouping in Early Vision
19886
16 20086
17 19885
18 19925
19
Neural networks for machine vision: learning three-dimensional object representations
19915
20 19923

About Michael Seibert

Michael Seibert is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Ocean Engineering and Aerospace Engineering, having authored 28 papers that have together received 688 indexed citations. Recurring topics across this work include Maritime Navigation and Safety (5 papers), Advanced Vision and Imaging (5 papers), Neural Networks and Applications (5 papers), Anomaly Detection Techniques and Applications (4 papers), Infrared Target Detection Methodologies (4 papers), CCD and CMOS Imaging Sensors (4 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Fault Detection and Control Systems (3 papers). The work is most often cited by research in Media Technology (144 citations), Computer Vision and Pattern Recognition (289 citations), Ocean Engineering (144 citations), Artificial Intelligence (229 citations) and Signal Processing (75 citations). Michael Seibert has collaborated with scholars based in United States, Sweden and Canada. Frequent co-authors include Allen M. Waxman, Neil A. Bomberger, Bradley J. Rhodes, Alan N. Gove, David A. Fay, Joseph P. Racamato, E. D. Savoye, Michael D. Beynon, Dan E. Dudgeon and J.P. Reilly. Their work appears in journals such as Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Signal Processing, Intelligent Decision Technologies and Defense Technical Information Center (DTIC).

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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