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.
Driver Inattention Monitoring System for Intelligent Vehicles: A Review
2011506 citationsZhencheng Hu, Keiichi Uchimura et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Keiichi Uchimura
Since
Specialization
Citations
This map shows the geographic impact of Keiichi Uchimura'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 Keiichi Uchimura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keiichi Uchimura more than expected).
Fields of papers citing papers by Keiichi Uchimura
This network shows the impact of papers produced by Keiichi Uchimura. 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 Keiichi Uchimura. The network helps show where Keiichi Uchimura may publish in the future.
Co-authorship network of co-authors of Keiichi Uchimura
This figure shows the co-authorship network connecting the top 25 collaborators of Keiichi Uchimura.
A scholar is included among the top collaborators of Keiichi Uchimura 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 Keiichi Uchimura. Keiichi Uchimura is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Rachmadi, Reza Fuad, et al.. (2017). Road sign classification system using cascade convolutional neural network. International journal of innovative computing, information & control. 13(1). 95–109.6 indexed citations
2.
Wijaya, I Gede Pasek Suta, Keiichi Uchimura, & Gou Koutaki. (2015). D-12-33 Pornographic Image Recognition using Eigenporn of HSV Skin Segmented Image. 2015(2). 85.1 indexed citations
Wijaya, I Gede Pasek Suta, et al.. (2012). The verification with real-World road network on optimization of traffic signal parameters using multi-element genetic algorithms. 19th ITS World CongressERTICO - ITS EuropeEuropean CommissionITS AmericaITS Asia-Pacific. 144.3 indexed citations
Wijaya, I Gede Pasek Suta, et al.. (2011). The traffic signal control using multi-element GA. IEICE Technical Report; IEICE Tech. Rep.. 110(420). 299–304.
8.
Koutaki, Gou, et al.. (2011). Image segmentation based on Edge detection using boundary code. International journal of innovative computing, information & control. 7(10). 6073–6083.12 indexed citations
Wijaya, I Gede Pasek Suta, Keiichi Uchimura, & Zhencheng Hu. (2008). Improving the Pose Invariant Face Recognition Using Double Frequency Analysis and Color Information. 2008. 615–615.3 indexed citations
12.
Hu, Zhencheng, et al.. (2007). VEHICLE DETECTION USING PROBABILITY FUSION MAPS GENERATED BY A MULTI-CAMERA SYSTEM(International Workshop on Advanced Image Technology 2007). 106(448). 51–56.2 indexed citations
13.
Uchimura, Keiichi, et al.. (2005). Construction of transport control system using congestion index. IEICE Technical Report; IEICE Tech. Rep.. 105(166). 23–28.1 indexed citations
Uchimura, Keiichi, et al.. (1996). Electromagnetic Radiation Noise from Surface Gas Discharges-Mechanisms of Propagation, Coupling and Formation. IEICE Transactions on Communications. 79(4). 490–496.1 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.