Akisato Kimura

1.1k citations
67 papers · 602 indexed · h-index 14

Akisato Kimura

56 papers receiving 582 citations

Peers

Akisato Kimura
Comparison fields: 5 of 78
  • Computational Mathematics 21
  • Computer Vision and Pattern Recognition 380
  • Signal Processing 102
  • Human-Computer Interaction 45
  • Artificial Intelligence 183
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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-authorship network

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
#Work
1 20250
2 20240
3 20243
4 20220
5
Permuton-induced Chinese Restaurant Process
20210
6 20216
7
Baxter Permutation Process
20202
8
Imitation networks: Few-shot learning of neural networks from scratch
20184
9 20156
10
Rectangular Tiling Process
201410
11
Non-negative multiple matrix factorization
201317
12
Change-point detection with feature selection in high-dimensional time-series data
201329
13
Social curation as corpora for large-scale multimedia content analysis
20133
14 201312
15
On the Image Segmentation Method
20121
16 20100
17
NTT Communication Science Laboratories and NII in TRECVID 2010 Instance Search Task
20102
18
Real-time estimation of human visual attention with MCMC-based particle filter
20092
19
Multiterminal source coding with complementary delivery
20089
20
A Quick Template Matching Algorithm with Adaptive Window-Skipping Using Inner-Subtemplates' Distances
20051

About Akisato Kimura

Akisato Kimura is a scholar working on Computer Vision and Pattern Recognition, Computational Mathematics and Signal Processing, having authored 67 papers that have together received 602 indexed citations. Recurring topics across this work include Video Analysis and Summarization (13 papers), Music and Audio Processing (12 papers), Visual Attention and Saliency Detection (9 papers), Advanced Image and Video Retrieval Techniques (9 papers), Speech and Audio Processing (6 papers), Time Series Analysis and Forecasting (6 papers), Advanced Vision and Imaging (6 papers) and Image Enhancement Techniques (6 papers). The work is most often cited by research in Computational Mathematics (21 citations), Computer Vision and Pattern Recognition (380 citations) and Signal Processing (102 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, Makoto Yamada, Hiroshi Sawada, Koh Takeuchi, Xiaomeng Wu, Tomoharu Iwata and Takatsugu Hirayama. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Audio Speech and Language Processing, Monthly Notices of the Royal Astronomical Society, Signal Processing and The Computer Journal.

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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