Howard Zhou

1.6k total citations · 1 hit paper
13 papers, 760 citations indexed

About

Howard Zhou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Oncology. According to data from OpenAlex, Howard Zhou has authored 13 papers receiving a total of 760 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 4 papers in Oncology. Recurrent topics in Howard Zhou's work include Advanced Vision and Imaging (4 papers), AI in cancer detection (4 papers) and Cutaneous Melanoma Detection and Management (4 papers). Howard Zhou is often cited by papers focused on Advanced Vision and Imaging (4 papers), AI in cancer detection (4 papers) and Cutaneous Melanoma Detection and Management (4 papers). Howard Zhou collaborates with scholars based in United States, China and Brazil. Howard Zhou's co-authors include James M. Rehg, Zhicheng Wang, Kyle Genova, Jonathan T. Barron, Qianqian Wang, Noah Snavely, Pratul P. Srinivasan, Ricardo Martin-Brualla, Thomas Funkhouser and Greg Turk and has published in prestigious journals such as IEEE Transactions on Visualization and Computer Graphics, Journal of Food Quality and PubMed.

In The Last Decade

Howard Zhou

12 papers receiving 739 citations

Hit Papers

IBRNet: Learning Multi-View Image-Based Rendering 2021 2026 2022 2024 2021 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
Howard Zhou United States 7 537 365 229 111 95 13 760
David Stutz Germany 9 325 0.6× 71 0.2× 114 0.5× 118 1.1× 8 0.1× 15 600
R. Wegenkittl Austria 17 463 0.9× 328 0.9× 207 0.9× 56 0.5× 16 0.2× 36 727
Annick Montanvert France 12 702 1.3× 210 0.6× 169 0.7× 48 0.4× 3 0.0× 24 900
Alexander Bornik Austria 15 376 0.7× 109 0.3× 85 0.4× 59 0.5× 6 0.1× 39 566
Philippe Lacroute United States 5 619 1.2× 679 1.9× 397 1.7× 15 0.1× 17 0.2× 5 837
Satoshi Tanaka Japan 12 129 0.2× 103 0.3× 118 0.5× 21 0.2× 9 0.1× 91 505
Weiliang Meng China 15 410 0.8× 31 0.1× 60 0.3× 206 1.9× 19 0.2× 84 716
Philippe-Henri Gosselin France 14 442 0.8× 21 0.1× 73 0.3× 125 1.1× 23 0.2× 36 593
Yu‐Chi Lai Taiwan 13 220 0.4× 98 0.3× 34 0.1× 30 0.3× 6 0.1× 43 433
Itsuo Kumazawa Japan 10 206 0.4× 26 0.1× 65 0.3× 106 1.0× 6 0.1× 101 512

Countries citing papers authored by Howard Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Howard Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Howard Zhou

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

All Works

13 of 13 papers shown
2.
Alldrin, Neil, Otilia Stretcu, Hao Xiong, et al.. (2024). Modeling Collaborator: Enabling Subjective Vision Classification with Minimal Human Effort via LLM Tool-Use. 17553–17563. 5 indexed citations
3.
Zhou, Howard, et al.. (2024). LFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals. 11–20. 3 indexed citations
4.
Mensink, Thomas, Jasper Uijlings, Lluís Castrejón, et al.. (2023). Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories. 3090–3101. 6 indexed citations
5.
Wang, Qianqian, Zhicheng Wang, Kyle Genova, et al.. (2021). IBRNet: Learning Multi-View Image-Based Rendering. 4688–4697. 393 indexed citations breakdown →
6.
Zou, Le, Howard Zhou, Samuel Cheng, & Chuan He. (2010). Dual Range Deringing for non-blind image deconvolution. 27. 1701–1704. 2 indexed citations
7.
Zhou, Howard, James M. Rehg, & Mei Chen. (2010). Exemplar-based segmentation of pigmented skin lesions from dermoscopy images. 225–228. 11 indexed citations
8.
Zhou, Howard, et al.. (2010). Movie genre classification via scene categorization. 747–750. 80 indexed citations
9.
Zhou, Howard, et al.. (2009). Dermoscopic interest point detector and descriptor. 1318–1321. 18 indexed citations
10.
Zhou, Howard, Mei Chen, Richard Gass, et al.. (2008). Spatially constrained segmentation of dermoscopy images. 800–803. 37 indexed citations
11.
Zhou, Howard, Mei Chen, Richard Gass, et al.. (2008). Feature-preserving artifact removal from dermoscopy images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6914. 69141B–69141B. 47 indexed citations
12.
Zhou, Howard, Jie Sun, Greg Turk, & James M. Rehg. (2007). Terrain Synthesis from Digital Elevation Models. IEEE Transactions on Visualization and Computer Graphics. 13(4). 834–848. 157 indexed citations
13.
Qin, Lingfeng, et al.. (1997). [Computer image analysis of 20 tiny medicinal seeds].. PubMed. 22(3). 137–9, 190. 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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