Matthew D. Mullin
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 10%
- Human-Computer Interaction top 5%
- Media Technology top 5%
- Signal Processing top 10%
- Co-authors
- Rahul SukthankarJianxin WuJames M. RehgS. Charles BrubakerTerence SimShumeet BalujaJoseph A. GallianFrançois Laviolette
- Topics
- Face and Expression Recognition (7 papers)Machine Learning and Algorithms (4 papers)Augmented Reality Applications (2 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceInternational Journal of Computer VisionJournal of Machine Learning Research
- Partner nations
- United States
In The Last Decade
Matthew D. Mullin
11 papers receiving 597 citations
Peers
Comparison fields: 5 of 82
- Computer Vision and Pattern Recognition 496
- Artificial Intelligence 152
- Human-Computer Interaction 123
- Media Technology 74
- Signal Processing 62
Countries citing papers authored by Matthew D. Mullin
This map shows the geographic impact of Matthew D. Mullin'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 Matthew D. Mullin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew D. Mullin more than expected).
Fields of papers citing papers by Matthew D. Mullin
This network shows the impact of papers produced by Matthew D. Mullin. 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 Matthew D. Mullin. The network helps show where Matthew D. Mullin may publish in the future.
Co-authorship network of co-authors of Matthew D. Mullin
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew D. Mullin. A scholar is included among the top collaborators of Matthew D. Mullin 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 Matthew D. Mullin. Matthew D. Mullin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 149 | |
| 2 | 2 | |
| 3 | 99 | |
| 4 | 35 | |
| 5 | Learning a Rare Event Detection Cascade by Direct Feature Selection | 80 |
| 6 | 47 | |
| 7 | 158 | |
| 8 | Complete Cross-Validation for Nearest Neighbor Classifiers | 61 |
| 9 | Self-Calibrating Camera-Assisted Presentation Interface | 14 |
| 10 | An Efficient Technique for Calculating Exact Nearest-Neighbor Classification Accuracy | 3 |
| 11 | 9 |
About Matthew D. Mullin
Matthew D. Mullin is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Discrete Mathematics and Combinatorics, having authored 11 papers that have together received 657 indexed citations. Recurring topics across this work include Face and Expression Recognition (7 papers), Machine Learning and Algorithms (4 papers) and Augmented Reality Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (496 citations), Human-Computer Interaction (123 citations) and Media Technology (74 citations). Matthew D. Mullin has collaborated with scholars based in United States. Frequent co-authors include Rahul Sukthankar, Jianxin Wu, James M. Rehg, S. Charles Brubaker, Terence Sim, Shumeet Baluja, Joseph A. Gallian, François Laviolette, Mario Marchand and John Shawe‐Taylor. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and Journal of Machine Learning Research.
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.