Injoon Hong

407 total citations
37 papers, 323 citations indexed

About

Injoon Hong is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Aerospace Engineering. According to data from OpenAlex, Injoon Hong has authored 37 papers receiving a total of 323 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 23 papers in Electrical and Electronic Engineering and 7 papers in Aerospace Engineering. Recurrent topics in Injoon Hong's work include CCD and CMOS Imaging Sensors (21 papers), Advanced Image and Video Retrieval Techniques (14 papers) and Advanced Memory and Neural Computing (9 papers). Injoon Hong is often cited by papers focused on CCD and CMOS Imaging Sensors (21 papers), Advanced Image and Video Retrieval Techniques (14 papers) and Advanced Memory and Neural Computing (9 papers). Injoon Hong collaborates with scholars based in South Korea, United States and Canada. Injoon Hong's co-authors include Hoi‐Jun Yoo, Gyeonghoon Kim, Youchang Kim, Kyeongryeol Bong, Kyuho Lee, Seong‐Wook Park, Jinwook Oh, Seungjin Lee, Junyoung Park and Dongjoo Shin and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IEEE Transactions on Circuits and Systems I Regular Papers and IEEE Micro.

In The Last Decade

Injoon Hong

33 papers receiving 318 citations

Peers

Injoon Hong

EEE

CVPR

CNC

Gyeonghoon Kim South Korea

Hyunsurk Ryu South Korea

M. Höller United States

W. Bruce Culbertson United States

Muhammad Iram Baig Pakistan

Manuel Eggimann Switzerland

Shyh-Yih Ma Taiwan

Ryotaro Taniguchi Japan

Tuan Nghia Nguyen South Korea

Marco Indaco Italy

Gyeonghoon Kim South Korea

Injoon Hong

190 ×1.0

EEE

173 ×1.1

CVPR

55 ×1.0

37 ×0.8

CNC

18 ×0.5

Citations per year, relative to Injoon Hong Injoon Hong (= 1×) peers Gyeonghoon Kim

Countries citing papers authored by Injoon Hong

Since Specialization

Citations

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

Fields of papers citing papers by Injoon Hong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Injoon Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Injoon Hong. A scholar is included among the top collaborators of Injoon Hong 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 Injoon Hong. Injoon Hong 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:

20 of 20 papers shown

Lee, Jinmook, Seong‐Wook Park, Injoon Hong, & Hoi‐Jun Yoo. (2016). An 8.3mW 1.6Msamples/s multi-modal event-driven speech enhancement processor for robust speech recognition in smart glasses. 50. 117–120. 2 indexed citations

Kim, Youchang, Injoon Hong, Jun-Young Park, & Hoi‐Jun Yoo. (2016). A 0.5 V 54 Ultra-Low-Power Object Matching Processor for Micro Air Vehicle Navigation. IEEE Transactions on Circuits and Systems I Regular Papers. 63(3). 359–369. 9 indexed citations

Hong, Injoon, Jason Clemons, Rangharajan Venkatesan, et al.. (2016). A real-time energy-efficient superpixel hardware accelerator for mobile computer vision applications. 1–6. 6 indexed citations

Hong, Injoon, Kyeongryeol Bong, Dongjoo Shin, et al.. (2015). 18.1 A 2.71nJ/pixel 3D-stacked gaze-activated object-recognition system for low-power mobile HMD applications. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–3. 16 indexed citations

Hong, Injoon, Kyeongryeol Bong, Dongjoo Shin, et al.. (2015). A 2.71 nJ/Pixel Gaze-Activated Object Recognition System for Low-Power Mobile Smart Glasses. IEEE Journal of Solid-State Circuits. 51(1). 45–55. 17 indexed citations

Hong, Injoon, Dongjoo Shin, Youchang Kim, et al.. (2015). A keypoint-level parallel pipelined object recognition processor with gaze activation image sensor for mobile smart glasses system. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–3. 1 indexed citations

Lee, Jinmook, Seong‐Wook Park, Injoon Hong, & Hoi‐Jun Yoo. (2015). A 3.13nJ/sample energy-efficient speech extraction processor for robust speech recognition in mobile head-mounted display systems. 103. 1790–1793. 3 indexed citations

Hong, Injoon, Gyeonghoon Kim, Youchang Kim, et al.. (2015). A 27 mW Reconfigurable Marker-Less Logarithmic Camera Pose Estimation Engine for Mobile Augmented Reality Processor. IEEE Journal of Solid-State Circuits. 50(11). 2513–2523. 10 indexed citations

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

10.

Hong, Injoon, Gyeonghoon Kim, Youchang Kim, et al.. (2014). A 27mW reconfigurable marker-less logarithmic camera pose estimation engine for mobile augmented reality processor. 57. 209–212. 2 indexed citations

11.

Kim, Gyeonghoon, Kyuho Lee, Youchang Kim, et al.. (2014). A 1.22 TOPS and 1.52 mW/MHz Augmented Reality Multicore Processor With Neural Network NoC for HMD Applications. IEEE Journal of Solid-State Circuits. 50(1). 113–124. 28 indexed citations

12.

Kim, Gyeonghoon, Seong‐Wook Park, Kyuho Lee, et al.. (2014). A task-level pipelined many-SIMD augmented reality processor with congestion-aware network-on-chip scheduler. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 1–3.

13.

Park, Jun-Young, et al.. (2013). A 32.8mW 60fps cortical vision processor for spatio-temporal action recognition. 2. 1002–1005. 1 indexed citations

14.

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

15.

Hong, Injoon, et al.. (2013). A 646GOPS/W multi-classifier many-core processor with cortex-like architecture for super-resolution recognition. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 168–169. 35 indexed citations

16.

Hong, Injoon, et al.. (2013). Intelligent Network-on-Chip With Online Reinforcement Learning for Portable HD Object Recognition Processor. IEEE Transactions on Circuits and Systems I Regular Papers. 61(2). 476–484. 8 indexed citations

17.

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

18.

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

19.

Oh, Jinwook, et al.. (2012). A 320mW 342GOPS real-time moving object recognition processor for HD 720p video streams. 220–222. 27 indexed citations

20.

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

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.

Explore authors with similar magnitude of impact