Yeon-Ho Kim

958 total citations
12 papers, 783 citations indexed

About

Yeon-Ho Kim is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Control and Systems Engineering. According to data from OpenAlex, Yeon-Ho Kim has authored 12 papers receiving a total of 783 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 4 papers in Biomedical Engineering and 2 papers in Control and Systems Engineering. Recurrent topics in Yeon-Ho Kim's work include Human Pose and Action Recognition (8 papers), Video Surveillance and Tracking Methods (7 papers) and Gait Recognition and Analysis (4 papers). Yeon-Ho Kim is often cited by papers focused on Human Pose and Action Recognition (8 papers), Video Surveillance and Tracking Methods (7 papers) and Gait Recognition and Analysis (4 papers). Yeon-Ho Kim collaborates with scholars based in South Korea and United States. Yeon-Ho Kim's co-authors include Ahmad Jalal, Daijin Kim, Shaharyar Kamal, Yong-Joong Kim, Aleix M. Martı́nez, A.C. Kak, Jin S. Lee, Minsu Cho, Doyeon Kim and Seung-Hyun Kim and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and Pattern Recognition Letters.

In The Last Decade

Yeon-Ho Kim

12 papers receiving 764 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yeon-Ho Kim South Korea	9	656	216	163	96	57	12	783
Imran N. Junejo United Arab Emirates	14	668 1.0×	350 1.6×	168 1.0×	81 0.8×	27 0.5×	46	787
Jianqin Yin China	14	555 0.8×	250 1.2×	216 1.3×	160 1.7×	33 0.6×	84	792
Abdulwahab Alazeb Saudi Arabia	16	392 0.6×	171 0.8×	104 0.6×	60 0.6×	43 0.8×	50	749
Matías Mendieta United States	9	460 0.7×	232 1.1×	152 0.9×	134 1.4×	26 0.5×	13	727
Weizhi Nie China	11	569 0.9×	277 1.3×	143 0.9×	90 0.9×	21 0.4×	26	711
Qieshi Zhang China	14	423 0.6×	156 0.7×	143 0.9×	90 0.9×	119 2.1×	91	685
Donghao Luo China	11	755 1.2×	345 1.6×	122 0.7×	37 0.4×	33 0.6×	33	931
Ju Shen United States	11	605 0.9×	114 0.5×	163 1.0×	147 1.5×	37 0.6×	39	800
Sang Min Yoon South Korea	10	481 0.7×	87 0.4×	133 0.8×	46 0.5×	39 0.7×	50	608
Chih‐Yao Ma United States	11	734 1.1×	413 1.9×	75 0.5×	53 0.6×	39 0.7×	18	898

Countries citing papers authored by Yeon-Ho Kim

Since Specialization

Citations

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

Fields of papers citing papers by Yeon-Ho Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yeon-Ho Kim. 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 Yeon-Ho Kim. The network helps show where Yeon-Ho Kim may publish in the future.

Co-authorship network of co-authors of Yeon-Ho Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Yeon-Ho Kim. A scholar is included among the top collaborators of Yeon-Ho Kim 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 Yeon-Ho Kim. Yeon-Ho Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

Kim, Yeon-Ho & Daijin Kim. (2020). A CNN-based 3D human pose estimation based on projection of depth and ridge data. Pattern Recognition. 106. 107462–107462. 30 indexed citations

Kim, Yeon-Ho & Daijin Kim. (2018). Real-time dance evaluation by markerless human pose estimation. Multimedia Tools and Applications. 77(23). 31199–31220. 18 indexed citations

Kim, Yeon-Ho, et al.. (2018). First Person Action Recognition via Two-stream ConvNet with Long-term Fusion Pooling. Pattern Recognition Letters. 112. 161–167. 31 indexed citations

Kim, Seung-Hyun, et al.. (2017). Training Network Design Based on Convolution Neural Network for Object Classification in few class problem. The Journal of the Korean Institute of Information and Communication Engineering. 21(1). 144–150. 3 indexed citations

Jalal, Ahmad, Yeon-Ho Kim, Yong-Joong Kim, Shaharyar Kamal, & Daijin Kim. (2016). Robust human activity recognition from depth video using spatiotemporal multi-fused features. Pattern Recognition. 61. 295–308. 301 indexed citations

Kim, Yeon-Ho & Daijin Kim. (2015). Efficient body part tracking using ridge data and data pruning. 73. 114–120. 5 indexed citations

Jalal, Ahmad, et al.. (2015). Human daily activity recognition with joints plus body features representation using Kinect sensor. 14. 1–6. 51 indexed citations

Jalal, Ahmad, Yeon-Ho Kim, & Daijin Kim. (2014). Ridge body parts features for human pose estimation and recognition from RGB-D video data. 1–6. 133 indexed citations

Jalal, Ahmad & Yeon-Ho Kim. (2014). Dense depth maps-based human pose tracking and recognition in dynamic scenes using ridge data. 119–124. 125 indexed citations

10.

Yun, Chulhee, et al.. (2013). A fusion of computer vision technique and a visual programming language for edutainment robots. 1. 1–5. 1 indexed citations

11.

Kim, Yeon-Ho & A.C. Kak. (2006). Error analysis of robust optical flow estimation by least median of squares methods for the varying illumination model. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(9). 1418–1435. 18 indexed citations

12.

Kim, Yeon-Ho, et al.. (2005). Robust motion estimation under varying illumination. Image and Vision Computing. 23(4). 365–375. 67 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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