Dong‐Joong Kang

1.2k citations
70 papers · 904 indexed · h-index 17
Topics
Video Surveillance and Tracking Methods (14 papers)Industrial Vision Systems and Defect Detection (12 papers)Image and Object Detection Techniques (11 papers)

In The Last Decade

Dong‐Joong Kang

64 papers receiving 862 citations

Peers

Dong‐Joong Kang
Comparison fields: 5 of 96
  • Computer Vision and Pattern Recognition 458
  • Industrial and Manufacturing Engineering 283
  • Mechanical Engineering 158
  • Automotive Engineering 142
  • Media Technology 141
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Citations per field
00.5×4.4×
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Citations per year

Countries citing papers authored by Dong‐Joong Kang

Since Specialization
Citations

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

Fields of papers citing papers by Dong‐Joong Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dong‐Joong Kang. 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 Dong‐Joong Kang. The network helps show where Dong‐Joong Kang may publish in the future.

Co-authorship network of co-authors of Dong‐Joong Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Dong‐Joong Kang. A scholar is included among the top collaborators of Dong‐Joong Kang 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 Dong‐Joong Kang. Dong‐Joong Kang 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
#WorkIndexed citations
1 2
2 3
3 1
4 2
5 16
6 27
7 16
8
Localization of Welding Defects using a Weakly Supervised Neural Network
2
9
Vibration Control of a Vehicle Active Suspension System Using a DDPG Algorithm
4
10
Finding a High Accuracy Neural Network for the Welding Defects Classification Using Efficient Neural Architecture Search via Parameter Sharing
1
11 1
12 11
13 3
14
Fast defect detection algorithm on the variety surface with random forest using GPUs
2
15 3
16
Development of 3D Visual Underwater Landmark Recognition Method For AUVs
1
17
A Recognition Algorithm of Suspicious Human Behaviors using Hidden Markov Models in an Intelligent Surveillance System
0
18
Detection of Calibration Patterns for Camera Calibration with Irregular Lighting and Complicated Backgrounds
11
19
Fast Stereo Matching Algorithm using Edge Projection
4
20
Moving Vehicle Segmentation from Plane Constraint
4

About Dong‐Joong Kang

Dong‐Joong Kang is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Industrial and Manufacturing Engineering, having authored 70 papers that have together received 904 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (14 papers), Industrial Vision Systems and Defect Detection (12 papers) and Image and Object Detection Techniques (11 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (283 citations), Computer Vision and Pattern Recognition (458 citations) and Media Technology (141 citations). Dong‐Joong Kang has collaborated with scholars based in South Korea, China and United Kingdom. Frequent co-authors include Jun-Hyub Park, Teng Zhu, Jong‐Seob Won, Jeonghyun Kim, Jeonghyun Kim, Feifei Chen, Hak‐Joo Lee, Son‐Cheol Yu, Baopeng Zhang and Jin Young Kim. Their work appears in journals such as IEEE Access, Pattern Recognition and Journal of Physics D Applied Physics.

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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