Michael S. Brown

5.9k total citations · 2 hit papers
129 papers, 3.6k citations indexed

About

Michael S. Brown is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Media Technology. According to data from OpenAlex, Michael S. Brown has authored 129 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Computer Vision and Pattern Recognition, 35 papers in Atomic and Molecular Physics, and Optics and 20 papers in Media Technology. Recurrent topics in Michael S. Brown's work include Advanced Vision and Imaging (23 papers), Image Enhancement Techniques (20 papers) and Spectroscopy and Laser Applications (17 papers). Michael S. Brown is often cited by papers focused on Advanced Vision and Imaging (23 papers), Image Enhancement Techniques (20 papers) and Spectroscopy and Laser Applications (17 papers). Michael S. Brown collaborates with scholars based in United States, Singapore and Canada. Michael S. Brown's co-authors include Yu‐Wing Tai, Tat-Jun Chin, David Suter, Julio H. Zaragoza, Ruigang Yang, Jaesik Park, In-So Kweon, Hyeongwoo Kim, W. Brent Seales and Joseph L. Goldstein and has published in prestigious journals such as New England Journal of Medicine, Journal of Biological Chemistry and PLoS ONE.

In The Last Decade

Michael S. Brown

124 papers receiving 3.5k citations

Hit Papers

High quality depth map upsampling for 3D-TOF cameras 2011 2026 2016 2021 2011 2013 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael S. Brown United States 31 2.0k 734 463 358 333 129 3.6k
Hiroshi Murase Japan 32 3.5k 1.7× 483 0.7× 91 0.2× 754 2.1× 290 0.9× 389 5.5k
Michael Wand Germany 36 2.1k 1.0× 151 0.2× 216 0.5× 322 0.9× 338 1.0× 204 5.2k
Alexander A. Sawchuk United States 30 3.0k 1.4× 1.4k 2.0× 434 0.9× 373 1.0× 178 0.5× 160 4.7k
Nahum Kiryati Israel 35 2.8k 1.4× 754 1.0× 228 0.5× 401 1.1× 24 0.1× 115 3.9k
Frank Y. Shih United States 37 2.8k 1.4× 452 0.6× 192 0.4× 143 0.4× 47 0.1× 232 4.6k
Edmund Y. Lam Hong Kong 40 3.1k 1.5× 2.2k 3.0× 1.7k 3.6× 317 0.9× 107 0.3× 432 6.8k
Greg Slabaugh United Kingdom 30 1.9k 0.9× 267 0.4× 78 0.2× 173 0.5× 43 0.1× 145 4.4k
Yong Man Ro South Korea 35 3.4k 1.6× 796 1.1× 193 0.4× 115 0.3× 418 1.3× 379 4.9k
Gregory J. Ward United States 29 1.5k 0.7× 156 0.2× 584 1.3× 79 0.2× 87 0.3× 58 4.2k
Randal C. Nelson United States 25 1.4k 0.7× 195 0.3× 98 0.2× 427 1.2× 85 0.3× 79 2.4k

Countries citing papers authored by Michael S. Brown

Since Specialization
Citations

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

Fields of papers citing papers by Michael S. Brown

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael S. Brown. 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 Michael S. Brown. The network helps show where Michael S. Brown may publish in the future.

Co-authorship network of co-authors of Michael S. Brown

This figure shows the co-authorship network connecting the top 25 collaborators of Michael S. Brown. A scholar is included among the top collaborators of Michael S. Brown 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 Michael S. Brown. Michael S. Brown 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.
Vázquez-Corral, Javier, et al.. (2024). Palette-Based Color Harmonization via Color Naming. IEEE Signal Processing Letters. 31. 1474–1478. 1 indexed citations
2.
Brown, Michael S., et al.. (2022). Analyzing color imaging failure on consumer-grade cameras. Journal of the Optical Society of America A. 39(6). B21–B21. 2 indexed citations
3.
Afifi, Mahmoud, et al.. (2022). Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 82–90. 34 indexed citations
4.
Brown, Michael S., et al.. (2019). Improved tagged cardiac MRI myocardium strain analysis by leveraging cine segmentation. Computer Methods and Programs in Biomedicine. 184. 105128–105128. 6 indexed citations
5.
Afifi, Mahmoud & Michael S. Brown. (2019). Sensor-Independent Illumination Estimation for DNN Models.. arXiv (Cornell University). 282. 7 indexed citations
6.
Chu, Anne, et al.. (2017). Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults. PLoS ONE. 12(2). e0172535–e0172535. 99 indexed citations
7.
Nguyen, Rang & Michael S. Brown. (2017). RAW Image Reconstruction Using a Self-contained sRGB–JPEG Image with Small Memory Overhead. International Journal of Computer Vision. 126(6). 637–650. 17 indexed citations
8.
Brown, Michael S., Milan Biswal, Sukumar Brahma, Satish J. Ranade, & Huiping Cao. (2016). Characterizing and quantifying noise in PMU data. 1–5. 199 indexed citations
9.
Tai, Yu‐Wing, Xiaogang Chen, Seon Joo Kim, et al.. (2013). Nonlinear Camera Response Functions and Image Deblurring: Theoretical Analysis and Practice. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(10). 2498–2512. 67 indexed citations
10.
Roy, Sharmili, Xi Liang, Asanobu Kitamoto, et al.. (2013). Phenotype Detection in Morphological Mutant Mice Using Deformation Features. Lecture notes in computer science. 16(Pt 3). 437–444. 2 indexed citations
11.
Kim, Seon Joo, Hai Lin, Zheng Lu, et al.. (2012). A New In-Camera Imaging Model for Color Computer Vision and Its Application. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(12). 2289–2302. 107 indexed citations
12.
Brown, Michael S., et al.. (2011). Hot electron dominated rapid transverse ionization growth in liquid water. Optics Express. 19(13). 12241–12241. 2 indexed citations
13.
Tai, Yu‐Wing, Hao Du, Michael S. Brown, & Stephen Lin. (2009). Correction of Spatially Varying Image and Video Motion Blur Using a Hybrid Camera. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32(6). 1012–1028. 80 indexed citations
14.
Yang, Ruigang & Michael S. Brown. (2008). Proceedings of the 5th ACM/IEEE International Workshop on Projector camera systems. 2 indexed citations
15.
Tang, Chi–Keung, et al.. (2007). Example-Based Cosmetic Transfer. 211–218. 63 indexed citations
16.
Brown, Michael S.. (2004). Doing Adventure Education the 'Right' Way. 1(3). 33–46. 1 indexed citations
17.
Brown, Michael S., Yuanyuan Li, William L. Roberts, & James R. Gord. (2003). Analysis of transient-grating signals for reacting-flow applications. Applied Optics. 42(3). 566–566. 17 indexed citations
18.
Jaynes, Christopher, et al.. (2001). Dynamic shadow removal from front projection displays. IEEE Visualization. 175–182. 40 indexed citations
19.
Brown, Michael S. & William L. Roberts. (1997). <title>Thermometry in pressurized sooting flames using laser-induced gratings</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3172. 492–503. 1 indexed citations
20.
Brown, Michael S., M. H. Proffitt, & Lothar Frommhold. (1985). Collision-induced raman scattering by anisotropic molecules (H2, D2). Chemical Physics Letters. 117(3). 243–246. 19 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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