Joo Chan Lee

617 total citations · 1 hit paper
10 papers, 118 citations indexed

About

Joo Chan Lee is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Joo Chan Lee has authored 10 papers receiving a total of 118 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 3 papers in Computer Graphics and Computer-Aided Design and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Joo Chan Lee's work include Computer Graphics and Visualization Techniques (3 papers), Advanced Neural Network Applications (3 papers) and Advanced Vision and Imaging (2 papers). Joo Chan Lee is often cited by papers focused on Computer Graphics and Visualization Techniques (3 papers), Advanced Neural Network Applications (3 papers) and Advanced Vision and Imaging (2 papers). Joo Chan Lee collaborates with scholars based in South Korea. Joo Chan Lee's co-authors include Jong Hwan Ko, Eunbyung Park, SungTae Moon, Yongwoo Kim, Jung Taek Seo, Usman Ali and Kang Eun Jeon and has published in prestigious journals such as IEEE Internet of Things Journal, Electronics Letters and IEEE Transactions on Circuits and Systems I Regular Papers.

In The Last Decade

Joo Chan Lee

9 papers receiving 116 citations

Hit Papers

align trajectories

What are hit papers?

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.

Compact 3D Gaussian Representation for Radiance Field

2024 63 citations Joo Chan Lee, Jong Hwan Ko et al. profile →

Peers

Joo Chan Lee

CVPR

CGCD

CM

AE

IME

Levent Karacan Türkiye

Tal Remez Israel

Pierluigi Zama Ramirez Italy

Willi Menapace United States

Zorah Lähner Germany

Tomáš Jakab United Kingdom

Christian Reiser Germany

Daniel Soukup Austria

Abdelaziz Djelouah Switzerland

Éric Andrès France

Levent Karacan Türkiye

Joo Chan Lee

233 ×3.2

CVPR

22 ×0.6

CGCD

13 ×0.4

CM

9 ×0.7

AE

3 ×0.3

IME

Citations per year, relative to Joo Chan Lee Joo Chan Lee (= 1×) peers Levent Karacan

Countries citing papers authored by Joo Chan Lee

Since Specialization

Citations

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

Fields of papers citing papers by Joo Chan Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joo Chan Lee. 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 Joo Chan Lee. The network helps show where Joo Chan Lee may publish in the future.

Co-authorship network of co-authors of Joo Chan Lee

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

All Works

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10 of 10 papers shown

1.

Jeon, Kang Eun, et al.. (2024). KERNTROL: Kernel Shape Control Toward Ultimate Memory Utilization for In-Memory Convolutional Weight Mapping. IEEE Transactions on Circuits and Systems I Regular Papers. 71(12). 6138–6151.

2.

Lee, Joo Chan, et al.. (2024). Compact 3D Gaussian Representation for Radiance Field. 21719–21728. 63 indexed citations breakdown →

3.

Lee, Joo Chan, et al.. (2024). F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting. 7957–7965. 2 indexed citations

4.

Lee, Joo Chan, et al.. (2023). Masked Wavelet Representation for Compact Neural Radiance Fields. 20680–20690. 20 indexed citations

5.

Jeon, Kang Eun, et al.. (2023). Kernel Shape Control for Row-Efficient Convolution on Processing-In-Memory Arrays. 2 indexed citations

6.

Lee, Joo Chan, et al.. (2022). Scalable Color Quantization for Task-centric Image Compression. ACM Transactions on Multimedia Computing Communications and Applications. 19(2s). 1–18. 9 indexed citations

7.

Lee, Joo Chan, Yongwoo Kim, SungTae Moon, & Jong Hwan Ko. (2022). A Reconfigurable Neural Architecture for Edge–Cloud Collaborative Real-Time Object Detection. IEEE Internet of Things Journal. 9(23). 23390–23404. 9 indexed citations

8.

Lee, Joo Chan, Yongwoo Kim, SungTae Moon, & Jong Hwan Ko. (2021). A Splittable DNN-Based Object Detector for Edge-Cloud Collaborative Real-Time Video Inference. 1–8. 8 indexed citations

9.

Lee, Joo Chan, et al.. (2021). Robust detection of small and dense objects in images from autonomous aerial vehicles. Electronics Letters. 57(16). 611–613. 3 indexed citations

10.

Lee, Joo Chan, et al.. (2020). Detection and Analysis Technique for Manipulation Attacks on PLC Control Logic. 1–6. 2 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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