Akisato Kimura

1.1k citations
75 papers · 706 · h-index 15

Impact in

Papers in

Akisato Kimura

65 papers receiving 685 citations

Peers

Akisato Kimura
Comparison fields: 5 of 80
  • Computational Mathematics 25
  • Computer Vision and Pattern Recognition 450
  • Signal Processing 114
  • Human-Computer Interaction 58
  • Sensory Systems 37
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Yuchi Huang United States
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Dit–Yan Yeung Hong Kong
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Citations per field
00.5×9.3×
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Citations per year

Countries citing papers authored by Akisato Kimura

Since Specialization
Citations

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

Fields of papers citing papers by Akisato Kimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Akisato Kimura, 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 Akisato Kimura Line = papers co-authored together Akisato Kimura links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 75 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009104
2 201350
3 201343
4 201339
5 200837
6 201032
7
Change-point detection with feature selection in high-dimensional time-series data
201331
8 201927
9
Non-negative multiple matrix factorization
201319
10 201018
11 201217
12 200816
13 201915
14 202015
15 202214
16 201314
17 200913
18
Rectangular Tiling Process
201411
19 201910
20 202310

About Akisato Kimura

Akisato Kimura is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Human-Computer Interaction and Molecular Biology, having authored 75 papers that have together received 706 indexed citations. Recurring topics across this work include Music and Audio Processing (14 papers), Video Analysis and Summarization (14 papers), Advanced Image and Video Retrieval Techniques (12 papers), Visual Attention and Saliency Detection (11 papers), Time Series Analysis and Forecasting (7 papers), Image Enhancement Techniques (6 papers), Advanced Vision and Imaging (6 papers) and Speech and Audio Processing (6 papers). The work is most often cited by research in Computational Mathematics (25 citations), Computer Vision and Pattern Recognition (450 citations), Signal Processing (114 citations), Human-Computer Interaction (58 citations) and Sensory Systems (37 citations). Akisato Kimura has collaborated with scholars based in Japan, Canada and United States. Frequent co-authors include Kunio Kashino, Junji Yamato, Katsuhiko Ishiguro, Shigeru Takagi, Hiroshi Sawada, Makoto Yamada, Koh Takeuchi, Seiichi Uchida, Tomoharu Iwata and Ryo Yonetani. Their work appears in journals such as Pattern Recognition, The Computer Journal, IEEE Transactions on Audio Speech and Language Processing, Signal Processing and Therapeutic Delivery.

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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