Jinwook Oh

1.1k total citations
34 papers, 510 citations indexed

About

Jinwook Oh is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Jinwook Oh has authored 34 papers receiving a total of 510 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Electrical and Electronic Engineering, 20 papers in Computer Vision and Pattern Recognition and 9 papers in Artificial Intelligence. Recurrent topics in Jinwook Oh's work include CCD and CMOS Imaging Sensors (21 papers), Advanced Memory and Neural Computing (19 papers) and Advanced Image and Video Retrieval Techniques (11 papers). Jinwook Oh is often cited by papers focused on CCD and CMOS Imaging Sensors (21 papers), Advanced Memory and Neural Computing (19 papers) and Advanced Image and Video Retrieval Techniques (11 papers). Jinwook Oh collaborates with scholars based in South Korea and United States. Jinwook Oh's co-authors include Hoi‐Jun Yoo, Seungjin Lee, Joo-Young Kim, Joonsoo Kwon, Gyeonghoon Kim, Kwanho Kim, Minsu Kim, Minsu Kim, Seung‐Jin Lee and Junyoung Park and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IEEE Transactions on Very Large Scale Integration (VLSI) Systems and IEEE Micro.

In The Last Decade

Jinwook Oh

33 papers receiving 493 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jinwook Oh South Korea 13 324 239 95 75 69 34 510
Sungpill Choi South Korea 14 438 1.4× 324 1.4× 154 1.6× 36 0.5× 63 0.9× 36 682
Bertrand Zavidovique France 11 184 0.6× 483 2.0× 78 0.8× 63 0.8× 81 1.2× 114 752
Juhyoung Lee South Korea 17 444 1.4× 378 1.6× 188 2.0× 30 0.4× 116 1.7× 44 741
Shimpei Sato Japan 12 371 1.1× 310 1.3× 147 1.5× 28 0.4× 94 1.4× 52 588
Injoon Hong South Korea 11 195 0.6× 163 0.7× 26 0.3× 55 0.7× 33 0.5× 37 323
Hakan Yalcin United States 8 404 1.2× 192 0.8× 71 0.7× 48 0.6× 312 4.5× 10 760
Jinshan Yue China 15 689 2.1× 325 1.4× 187 2.0× 23 0.3× 199 2.9× 73 905
Zhengang Li United States 13 225 0.7× 238 1.0× 181 1.9× 51 0.7× 55 0.8× 41 512
Lingzhi Sui China 7 445 1.4× 493 2.1× 208 2.2× 27 0.4× 111 1.6× 11 712
Kuizhi Mei China 11 105 0.3× 247 1.0× 129 1.4× 37 0.5× 28 0.4× 63 426

Countries citing papers authored by Jinwook Oh

Since Specialization
Citations

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

Fields of papers citing papers by Jinwook Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinwook Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Jinwook Oh. A scholar is included among the top collaborators of Jinwook Oh 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 Jinwook Oh. Jinwook Oh 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.
Chen, Chun-Fu, Jinwook Oh, Quanfu Fan, & Marco Pistoia. (2018). SC-Conv: Sparse-Complementary Convolution for Efficient Model Utilization on CNNs. 97–100. 7 indexed citations
2.
Kim, Gyeonghoon, Youchang Kim, Kyuho Lee, et al.. (2014). 10.4 A 1.22TOPS and 1.52mW/MHz augmented reality multi-core processor with neural network NoC for HMD applications. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 182–183. 10 indexed citations
3.
Hong, Injoon, et al.. (2013). A 125,582 vector/s throughput and 95.1% accuracy ANN searching processor with Neuro-Fuzzy Vision Cache for real-time object recognition. 6 indexed citations
4.
Oh, Jinwook, Gyeonghoon Kim, Byeong‐Gyu Nam, & Hoi‐Jun Yoo. (2013). A 57 mW 12.5 µJ/Epoch Embedded Mixed-Mode Neuro-Fuzzy Processor for Mobile Real-Time Object Recognition. IEEE Journal of Solid-State Circuits. 48(11). 2894–2907. 13 indexed citations
5.
Park, Junyoung, et al.. (2012). A 92-mW Real-Time Traffic Sign Recognition System With Robust Illumination Adaptation and Support Vector Machine. IEEE Journal of Solid-State Circuits. 47(11). 2711–2723. 12 indexed citations
6.
Oh, Jinwook, Gyeonghoon Kim, Junyoung Park, et al.. (2012). A 320 mW 342 GOPS Real-Time Dynamic Object Recognition Processor for HD 720p Video Streams. IEEE Journal of Solid-State Circuits. 48(1). 33–45. 23 indexed citations
7.
Oh, Jinwook, Gyeonghoon Kim, Injoon Hong, et al.. (2012). Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems. IEEE Micro. 32(6). 38–50. 8 indexed citations
8.
Oh, Jinwook, et al.. (2012). A 320mW 342GOPS real-time moving object recognition processor for HD 720p video streams. 220–222. 27 indexed citations
9.
Oh, Jinwook, Seungjin Lee, & Hoi‐Jun Yoo. (2012). 1.2-mW Online Learning Mixed-Mode Intelligent Inference Engine for Low-Power Real-Time Object Recognition Processor. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 21(5). 921–933. 10 indexed citations
10.
Hong, Injoon, Jinwook Oh, & Hoi‐Jun Yoo. (2011). 1.15mW mixed-mode neuro-fuzzy accelerator for keypoint localization in image processing. 19. 1–4. 1 indexed citations
11.
Park, Jun-Young, Joonsoo Kwon, Jinwook Oh, Seungjin Lee, & Hoi‐Jun Yoo. (2011). A 92mW real-time traffic sign recognition system with robust light and dark adaptation. 2. 397–400. 7 indexed citations
12.
Lee, Seungjin, Jinwook Oh, Minsu Kim, et al.. (2010). A 345mW heterogeneous many-core processor with an intelligent inference engine for robust object recognition. 332–333. 23 indexed citations
13.
Kim, Joo-Young, Junyoung Park, Seungjin Lee, et al.. (2010). A 118.4 GB/s Multi-Casting Network-on-Chip With Hierarchical Star-Ring Combined Topology for Real-Time Object Recognition. IEEE Journal of Solid-State Circuits. 45(7). 1399–1409. 19 indexed citations
14.
Oh, Jinwook, Seungjin Lee, Minsu Kim, et al.. (2010). A 1.2mW on-line learning mixed mode intelligent inference engine for robust object recognition. 17–18. 14 indexed citations
15.
Kwon, Joonsoo, Minsu Kim, Jinwook Oh, & Hoi‐Jun Yoo. (2010). A 22.4 mW competitive fuzzy edge detection processor for volume rendering. 1883–1886. 2 indexed citations
16.
Kim, Joo-Young, et al.. (2010). A 201.4 GOPS 496 mW Real-Time Multi-Object Recognition Processor With Bio-Inspired Neural Perception Engine. IEEE Journal of Solid-State Circuits. 45(1). 32–45. 98 indexed citations
17.
Kim, Joo-Young, Min‐Su Kim, Seungjin Lee, et al.. (2009). Real-Time Object Recognition with Neuro-Fuzzy Controlled Workload-Aware Task Pipelining. IEEE Micro. 29(6). 28–43. 11 indexed citations
18.
Kim, Joo-Young, Kwanho Kim, Seungjin Lee, et al.. (2009). A 118.4GB/s multi-casting network-on-chip for real-time object recognition processor. 400–403. 3 indexed citations
19.
Kim, Min‐Su, Joo-Young Kim, Seungjin Lee, Jinwook Oh, & Hoi‐Jun Yoo. (2009). A 22.8GOPS 2.83mW neuro-fuzzy Object Detection Engine for fast multi-object recognition. 260–261. 8 indexed citations
20.
Oh, Jinwook, et al.. (2007). A Exploratory Study on the Performance Between Technology Innovation and Market Inclination in High-tech Enterprises. Journal of Digital Convergence. 5(1). 35–45. 1 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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