James K. Hahn

3.0k total citations
97 papers, 2.1k citations indexed

About

James K. Hahn is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Control and Systems Engineering. According to data from OpenAlex, James K. Hahn has authored 97 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Computer Vision and Pattern Recognition, 25 papers in Computer Graphics and Computer-Aided Design and 17 papers in Control and Systems Engineering. Recurrent topics in James K. Hahn's work include Computer Graphics and Visualization Techniques (24 papers), Human Motion and Animation (16 papers) and Advanced Vision and Imaging (15 papers). James K. Hahn is often cited by papers focused on Computer Graphics and Visualization Techniques (24 papers), Human Motion and Animation (16 papers) and Advanced Vision and Imaging (15 papers). James K. Hahn collaborates with scholars based in United States, South Korea and Spain. James K. Hahn's co-authors include John L. Sibert, Tapio Takala, Robert W. Lindeman, Larry Gritz, Rajat Mittal, Raymond J. Walsh, Steven Bielamowicz, Sung Yong Shin, Angelica I. Avilés-Rivero and Haoxiang Luo and has published in prestigious journals such as Journal of Computational Physics, The Journal of Urology and Journal of the American Ceramic Society.

In The Last Decade

James K. Hahn

92 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James K. Hahn United States 22 973 611 337 304 288 97 2.1k
S. Gottschalk Germany 10 1.1k 1.2× 439 0.7× 351 1.0× 133 0.4× 639 2.2× 19 2.0k
Hans Ingo Weber Brazil 20 380 0.4× 661 1.1× 310 0.9× 209 0.7× 386 1.3× 74 2.1k
Peter Allen United States 22 517 0.5× 886 1.5× 114 0.3× 175 0.6× 78 0.3× 57 1.7k
Jernej Barbič United States 24 896 0.9× 847 1.4× 1.1k 3.2× 185 0.6× 739 2.6× 66 2.3k
Pierre Boulanger Canada 24 801 0.8× 97 0.2× 206 0.6× 265 0.9× 139 0.5× 244 2.3k
Nigel W. John United Kingdom 23 831 0.9× 140 0.2× 279 0.8× 522 1.7× 150 0.5× 135 2.6k
Andrei State United States 22 1.1k 1.1× 71 0.1× 228 0.7× 601 2.0× 287 1.0× 58 1.6k
Denis Laurendeau Canada 21 975 1.0× 176 0.3× 336 1.0× 128 0.4× 243 0.8× 154 2.1k
Sabine Coquillart France 20 591 0.6× 275 0.5× 905 2.7× 426 1.4× 760 2.6× 40 2.0k
Nobuyuki Umetani Japan 20 376 0.4× 176 0.3× 524 1.6× 285 0.9× 419 1.5× 47 1.3k

Countries citing papers authored by James K. Hahn

Since Specialization
Citations

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

Fields of papers citing papers by James K. Hahn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James K. Hahn. 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 James K. Hahn. The network helps show where James K. Hahn may publish in the future.

Co-authorship network of co-authors of James K. Hahn

This figure shows the co-authorship network connecting the top 25 collaborators of James K. Hahn. A scholar is included among the top collaborators of James K. Hahn 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 James K. Hahn. James K. Hahn 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.
Wu, Xue, et al.. (2021). Pixel-wise body composition prediction with a multi-task conditional generative adversarial network. Journal of Biomedical Informatics. 120. 103866–103866. 5 indexed citations
2.
Wu, Xue, et al.. (2021). S2FLNet: Hepatic steatosis detection network with body shape. Computers in Biology and Medicine. 140. 105088–105088. 3 indexed citations
3.
Hudson, Geoffrey M., et al.. (2020). The Development of a BMI-Guided Shape Morphing Technique and the Effects of an Individualized Figure Rating Scale on Self-Perception of Body Size. European Journal of Investigation in Health Psychology and Education. 10(2). 579–594. 9 indexed citations
4.
Xiao, Xiao, et al.. (2020). A Physics-based Virtual Reality Simulation Framework for Neonatal Endotracheal Intubation. PubMed. 2020. 557–565. 10 indexed citations
5.
Zhao, Shang, Wei Li, Xiaoke Zhang, et al.. (2020). Automated Assessment System with Cross Reality for Neonatal Endotracheal Intubation Training. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). 2020. 738–739. 5 indexed citations
6.
Zhao, Shang, et al.. (2020). An Intelligent Augmented Reality Training Framework for Neonatal Endotracheal Intubation. PubMed. 2020. 672–681. 20 indexed citations
7.
Avilés-Rivero, Angelica I., et al.. (2019). ReTouchImg: Fusioning from-local-to-global context detection and graph data structures for fully-automatic specular reflection removal for endoscopic images. Computerized Medical Imaging and Graphics. 73. 39–48. 6 indexed citations
8.
Hahn, James K., et al.. (2019). Shape-based three-dimensional body composition extrapolation using multimodality registration. PubMed. 9. 64–64. 6 indexed citations
9.
Xiao, Xiao, et al.. (2018). Evaluation of performance, acceptance, and compliance of an auto-injector in healthy and rheumatoid arthritic subjects measured by a motion capture system. Patient Preference and Adherence. Volume 12. 515–526. 17 indexed citations
10.
Friedrich, Daniel, Lutz Dürselen, Bernd Mayer, et al.. (2017). Features of haptic and tactile feedback in TORS-a comparison of available surgical systems. Journal of Robotic Surgery. 12(1). 103–108. 18 indexed citations
11.
Luo, Haoxiang, Rajat Mittal, Xudong Zheng, et al.. (2008). An immersed-boundary method for flow–structure interaction in biological systems with application to phonation. Journal of Computational Physics. 227(22). 9303–9332. 153 indexed citations
12.
Kim, Jae Woo, et al.. (2006). Making Them Dance.. National Conference on Artificial Intelligence. 33–37. 3 indexed citations
13.
Hahn, James K., et al.. (2003). Combined Partial Motion Clips. Digital Library (University of West Bohemia). 9 indexed citations
14.
Manyak, Michael J., et al.. (2003). Virtual Reality Surgical Simulation for Lower Urinary Tract Endourologic Surgery. PubMed. 539(Pt B). 841–852. 3 indexed citations
15.
Narahari, Bhagirath, et al.. (2001). A real-time parallel scheduler for the imprecise computation model. Scalable Computing Practice and Experience. 2(1). 25–36. 2 indexed citations
16.
Hahn, James K., et al.. (1998). Integrating Sounds in Virtual Environments.. 7. 67–77. 4 indexed citations
17.
Hahn, James K., et al.. (1995). Sound For Animation And Virtual Reality. 1 indexed citations
18.
Hahn, James K., et al.. (1995). Mapping Motion to Sound and Music in Computer Animation and VE. 3 indexed citations
19.
Favre, Jean M. & James K. Hahn. (1994). An object oriented design for the visualization of multi-variable data objects. IEEE Visualization. 318–325. 4 indexed citations
20.
Hahn, James K.. (1988). Realistic animation of rigid bodies. ACM SIGGRAPH Computer Graphics. 22(4). 299–308. 245 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.

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