Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
GEINet: View-invariant gait recognition using a convolutional neural network
2016304 citationsYasushi Makihara, Daigo Muramatsu et al.profile →
Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition
2018298 citationsNoriko Takemura, Yasushi Makihara et al.SHILAP Revista de lepidopterologíaprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Daigo Muramatsu
Since
Specialization
Citations
This map shows the geographic impact of Daigo Muramatsu'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 Daigo Muramatsu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daigo Muramatsu more than expected).
This network shows the impact of papers produced by Daigo Muramatsu. 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 Daigo Muramatsu. The network helps show where Daigo Muramatsu may publish in the future.
Co-authorship network of co-authors of Daigo Muramatsu
This figure shows the co-authorship network connecting the top 25 collaborators of Daigo Muramatsu.
A scholar is included among the top collaborators of Daigo Muramatsu 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 Daigo Muramatsu. Daigo Muramatsu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hasegawa, R., Akira Uchiyama, Daigo Muramatsu, et al.. (2021). Developing a Close-Contact Detection System Using a Single Camera for Sports Considering Occlusion. IEICE Technical Report; IEICE Tech. Rep.. 121(41). 21–26.
Takemura, Noriko, Yasushi Makihara, Daigo Muramatsu, Tomio Echigo, & Yasushi Yagi. (2018). Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. SHILAP Revista de lepidopterología. 10(1).298 indexed citations breakdown →
Muramatsu, Daigo, et al.. (2010). Camera-based online signature verification system: effects of camera positions. World Automation Congress. 1–6.3 indexed citations
15.
Muramatsu, Daigo, et al.. (2006). A Sequential Monte Carlo Algorithm for Adaptation to Intersession Variability in On-line Signature Verification. HAL (Le Centre pour la Communication Scientifique Directe).4 indexed citations
16.
Muramatsu, Daigo, et al.. (2003). Bayesian MCMC for biometric person authentication incorporating on-line signature trajectories. International Conference on Signal Processing. 269–273.
17.
Muramatsu, Daigo & Takashi Matsumoto. (2003). An HMM On-Line Signature Verification with Pen Position Trajectories.. International Conference on Artificial Intelligence. 299–303.1 indexed citations
Hadidi, Khayrollah, et al.. (1999). A 500MS/sec–54dB THD S/H circuit in a 0.5µm CMOS process. European Solid-State Circuits Conference. 158–161.3 indexed citations
20.
Hadidi, Khayrollah, et al.. (1997). A 103MHz open-loop full CMOS highly-linear sample-and-hold amplifier. European Solid-State Circuits Conference. 396–399.1 indexed citations
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.