Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Faster and Better: A Machine Learning Approach to Corner Detection
20081.3k citationsEdward Rosten, Tom Drummond et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Fusing points and lines for high performance tracking
2005731 citationsEdward Rosten, Tom Drummondprofile →
Real-time visual tracking of complex structures
2002411 citationsTom Drummond, Roberto CipollaIEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Tom Drummond'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 Tom Drummond with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Drummond more than expected).
This network shows the impact of papers produced by Tom Drummond. 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 Tom Drummond. The network helps show where Tom Drummond may publish in the future.
Co-authorship network of co-authors of Tom Drummond
This figure shows the co-authorship network connecting the top 25 collaborators of Tom Drummond.
A scholar is included among the top collaborators of Tom Drummond 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 Tom Drummond. Tom Drummond is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cheng, Xuelian, Yiran Zhong, Mehrtash Harandi, et al.. (2020). Hierarchical Neural Architecture Search for Deep Stereo Matching. RMIT Research Repository (RMIT University Library). 33. 22158–22169.15 indexed citations
4.
Scheerlinck, Cedric, Davide Scaramuzza, Tom Drummond, et al.. (2020). How to Train Your Event Camera Neural Network. arXiv (Cornell University).5 indexed citations
5.
Newbury, R., et al.. (2020). Learning to Place Objects onto Flat Surfaces in Human-Preferred Orientations.. arXiv (Cornell University).1 indexed citations
6.
Drummond, Tom, et al.. (2019). Traversing Latent Space Using Decision Ferns. Lecture notes in computer science.2 indexed citations
7.
Drummond, Tom, et al.. (2017). Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation. 27–36.8 indexed citations
Reitmayr, Gerhard, et al.. (2010). Rapid 3D modelling from live video. International Convention on Information and Communication Technology, Electronics and Microelectronics. 252–257.3 indexed citations
Inoue, Akira, Tom Drummond, & Roberto Cipolla. (2001). Real Time Feature-Based Facial Tracking Using Lie Algebras. IEICE Transactions on Information and Systems. 84(12). 1733–1738.1 indexed citations
17.
Drummond, Tom & Roberto Cipolla. (2000). Real-time tracking of multiple articulated structures in multiple views. Lecture notes in computer science. 1843.34 indexed citations
18.
Horaud, Radu, et al.. (2000). Visually Guided Robots for Ship Building. HAL (Le Centre pour la Communication Scientifique Directe). 121(6). 221–5.2 indexed citations
19.
Drummond, Tom & Roberto Cipolla. (2000). Real-time tracking of complex structures for visual servoing. Lecture notes in computer science. 1883. 69–84.3 indexed citations
20.
Drummond, Tom, et al.. (1997). Segmentation is a high level process. Cambridge University Engineering Department Publications Database.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.