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.
A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement
2019276 citationsKenji Koide, Jun Miura et al.profile →
RDS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods
This map shows the geographic impact of Jun Miura'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 Jun Miura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Miura more than expected).
This network shows the impact of papers produced by Jun Miura. 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 Jun Miura. The network helps show where Jun Miura may publish in the future.
Co-authorship network of co-authors of Jun Miura
This figure shows the co-authorship network connecting the top 25 collaborators of Jun Miura.
A scholar is included among the top collaborators of Jun Miura 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 Jun Miura. Jun Miura is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ardiyanto, Igi & Jun Miura. (2017). GENERALIZED COVERAGE SOLVER USING HYBRID EVOLUTIONARY OPTIMIZATION. International journal of innovative computing, information & control. 13(3). 940.1 indexed citations
Miura, Jun, et al.. (2011). Improvement of a SIFT-based people identification for a people following robot:Use of a distance-dependent appearance model. 54. 27–27.
9.
Kidono, Kiyosumi, Akihiro Watanabe, Takashi Naito, & Jun Miura. (2011). Pedestrian Recognition Using High-definition LIDAR. Journal of the Robotics Society of Japan. 29(10). 963–970.3 indexed citations
10.
Miura, Jun, et al.. (2009). Multiple-Person Tracking for a Mobile Robot using Stereo. Machine Vision and Applications. 273–277.6 indexed citations
Miura, Jun, et al.. (2008). Person tracking for mobile robot using stereo camera. IEICE Technical Report; IEICE Tech. Rep.. 108(263). 37–42.1 indexed citations
13.
Shibata, Akihide & Jun Miura. (2008). Vision Planning for Object Search using Multiple Visual Features. 220–225.4 indexed citations
14.
Shirai, Yoshiaki, et al.. (2005). Segmentation of Sign Language for making HMM. 105. 55–60.1 indexed citations
15.
Miura, Jun, et al.. (2002). An Active Vision System for On-Line Traffic Sign Recognition. IEICE Transactions on Information and Systems. 85(11). 1784–1792.24 indexed citations
Miura, Jun, et al.. (2001). Tracking Multiple Pedestrians in Crowd. Transactions of the Institute of Systems Control and Information Engineers. 14(4). 180–185.9 indexed citations
18.
Kuno, Yoshinori, et al.. (1994). Object Recognition Using Conic-Based Invariants from Multiple Views.. Machine Vision and Applications. 17–20.
19.
Miura, Jun & Yoshiaki Shirai. (1993). An Uncertainty Model of Stereo Vision and Its Application to Vision-Motion Planning of Robot. International Joint Conference on Artificial Intelligence. 1618–1623.14 indexed citations
20.
Miura, Jun. (1991). Integration of problem solving and learning in intelligent robots. International Symposium on Robotics. 13–20.1 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.