Kazunori Iwata

666 total citations
46 papers, 516 citations indexed

About

Kazunori Iwata is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Geometry and Topology. According to data from OpenAlex, Kazunori Iwata has authored 46 papers receiving a total of 516 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 6 papers in Geometry and Topology. Recurrent topics in Kazunori Iwata's work include Image Retrieval and Classification Techniques (10 papers), Reinforcement Learning in Robotics (7 papers) and Medical Image Segmentation Techniques (5 papers). Kazunori Iwata is often cited by papers focused on Image Retrieval and Classification Techniques (10 papers), Reinforcement Learning in Robotics (7 papers) and Medical Image Segmentation Techniques (5 papers). Kazunori Iwata collaborates with scholars based in Japan, Germany and United States. Kazunori Iwata's co-authors include Tsukasa Seya, Shigeharu Nagasawa, Yusuke Yanagi, Masaru Okabe, John M. Pesando, Peter Johnson, Hiroyoshi Ariga, Shigeharu Ueda, Hideaki Sakai and Kazushi Ikeda and has published in prestigious journals such as Journal of Biological Chemistry, IEEE Transactions on Pattern Analysis and Machine Intelligence and Biochemical Journal.

In The Last Decade

Kazunori Iwata

39 papers receiving 501 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kazunori Iwata Japan 11 183 128 104 63 46 46 516
Nadav Rappoport Israel 12 505 2.8× 110 0.9× 42 0.4× 31 0.5× 54 1.2× 36 841
Delmiro Fernández-Reyes United Kingdom 18 252 1.4× 337 2.6× 217 2.1× 57 0.9× 48 1.0× 40 1.3k
Sumit Kumar Singh India 12 208 1.1× 183 1.4× 37 0.4× 14 0.2× 60 1.3× 44 662
Javier Rodríguez Colombia 16 323 1.8× 38 0.3× 52 0.5× 31 0.5× 32 0.7× 117 1.0k
Amir Foroushani Canada 9 717 3.9× 291 2.3× 134 1.3× 24 0.4× 108 2.3× 10 1.2k
Elkin Simson United States 10 150 0.8× 29 0.2× 41 0.4× 59 0.9× 47 1.0× 15 509
Wataru Fukuda Japan 16 127 0.7× 261 2.0× 183 1.8× 104 1.7× 24 0.5× 57 881
Grégory Nuel France 17 389 2.1× 72 0.6× 40 0.4× 13 0.2× 110 2.4× 58 758
Fabien Crauste France 18 303 1.7× 255 2.0× 61 0.6× 162 2.6× 92 2.0× 51 1.1k
Philipp Mueller Switzerland 14 320 1.7× 319 2.5× 178 1.7× 14 0.2× 57 1.2× 27 1.1k

Countries citing papers authored by Kazunori Iwata

Since Specialization
Citations

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

Fields of papers citing papers by Kazunori Iwata

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kazunori Iwata

This figure shows the co-authorship network connecting the top 25 collaborators of Kazunori Iwata. A scholar is included among the top collaborators of Kazunori Iwata 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 Kazunori Iwata. Kazunori Iwata 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.
Iwata, Kazunori, et al.. (2025). Making clustering methods workable for shapes using the ordinary Procrustes sum of squares. Pattern Recognition. 169. 111878–111878. 1 indexed citations
2.
Iwata, Kazunori. (2024). Use of several non-Euclidean metrics to compute distances between every two points in a plane bounded convex set. Journal of Computational Science. 85. 102494–102494.
3.
Iwata, Kazunori. (2020). EM Algorithm for Mixture Models in Shape Analysis. IEICE Technical Report; IEICE Tech. Rep.. 119(476). 1–7. 1 indexed citations
4.
Iwata, Kazunori, et al.. (2017). A sampling method for processing contours drawn with an uncertain stroke order and number. 468–471. 1 indexed citations
5.
Iwata, Kazunori. (2016). Extending the Peak Bandwidth of Parameters for Softmax Selection in Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems. 28(8). 1865–1877. 14 indexed citations
6.
Iwata, Kazunori, et al.. (2013). Image Segmentation Using a Spatially Correlated Mixture Model with Gaussian Process Priors. 59–63. 1 indexed citations
7.
Iwata, Kazunori. (2011). An information-theoretic analysis of return maximization in reinforcement learning. Neural Networks. 24(10). 1074–1081. 2 indexed citations
8.
Iwata, Kazunori, et al.. (2009). Matching between Piecewise Similar Curve Images. 3. 251–3. 1 indexed citations
9.
Ono, Kenji, et al.. (2009). An action-selection strategy insensitive to parameter-settings in reinforcement learning. 2009 ICCAS-SICE. 1012–1017. 1 indexed citations
10.
Iwata, Kazunori. (2006). Direct and rapid analysis of additives in polymer by GPC-MS. LCGC North America. 52–52. 1 indexed citations
11.
Kravchenko, Vladimir V., Gunnar F. Kaufmann, John C. Mathison, et al.. (2006). N-(3-Oxo-acyl)homoserine Lactones Signal Cell Activation through a Mechanism distinct from the Canonical Pathogen-associated Molecular Pattern Recognition Receptor Pathways. Journal of Biological Chemistry. 281(39). 28822–28830. 103 indexed citations
12.
Iwata, Kazunori, Kazushi Ikeda, & Hideaki Sakai. (2005). The asymptotic equipartition property in reinforcement learning and its relation to return maximization. Neural Networks. 19(1). 62–75. 4 indexed citations
13.
Iwata, Kazunori, Kazushi Ikeda, & Hideaki Sakai. (2004). On the Effect of Conditions among Agents in Multi-Agent Reinforcement Learning. 104(225). 25–30. 1 indexed citations
14.
Iwata, Kazunori, Kazushi Ikeda, & Hideaki Sakai. (2004). A New Criterion Using Information Gain for Action Selection Strategy in Reinforcement Learning. IEEE Transactions on Neural Networks. 15(4). 792–799. 21 indexed citations
15.
Namita, Yoshio, et al.. (2002). Seismic Proving Test of Eroded Piping: Program and Preliminary Analysis of Eroded Piping Tests. 91–97. 1 indexed citations
16.
Abe, Yoshihiro, et al.. (1999). Enantioselective binding sites on bovine serum albumin to dansyl amino acids. Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology. 1433(1-2). 188–197. 21 indexed citations
17.
Seya, Tsukasa, Kazunori Iwata, Yusuke Yanagi, et al.. (1997). The CD46 transmembrane domain is required for efficient formation of measles-virus-mediated syncytium. Biochemical Journal. 322(1). 135–144. 14 indexed citations
18.
Iwata, Kazunori, Tsukasa Seya, Yusuke Yanagi, et al.. (1995). Diversity of Sites for Measles Virus Binding and for Inactivation of Complement C3b and C4b on Membrane Cofactor Protein CD46. Journal of Biological Chemistry. 270(25). 15148–15152. 118 indexed citations
19.
Albeverio, S., et al.. (1992). Moments of random fields over a family of elliptic curves, and modular forms. EPrints - Department of Mathematics, Hokkaido University. 163. 1–9.
20.
Iwata, Kazunori, et al.. (1990). Dynamic Behavior Of Submerged Tension-Moored Floating Structure With Pressurized Air-Chamber And Wave Transformation.

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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