Marc‐André Carbonneau
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Radiology, Nuclear Medicine and Imaging
- Oncology
- Co-authors
- Ghyslain GagnonÉric GrangerVeronika CheplyginaYlva FerstlHerman KamperAlexandre J. RaymondDaniel HoldenNikolaus F. Troje
- Topics
- Speech Recognition and Synthesis (6 papers)Speech and Audio Processing (4 papers)Video Analysis and Summarization (4 papers)
- Journals
- Pattern RecognitionIEEE Transactions on Neural Networks and Learning SystemsIEEE Signal Processing Letters
- Partner nations
- CanadaNetherlandsSouth Africa
In The Last Decade
Marc‐André Carbonneau
16 papers receiving 696 citations
Hit Papers
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 374
- Computer Vision and Pattern Recognition 307
- Signal Processing 108
- Radiology, Nuclear Medicine and Imaging 102
- Oncology 47
Countries citing papers authored by Marc‐André Carbonneau
This map shows the geographic impact of Marc‐André Carbonneau'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 Marc‐André Carbonneau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc‐André Carbonneau more than expected).
Fields of papers citing papers by Marc‐André Carbonneau
This network shows the impact of papers produced by Marc‐André Carbonneau. 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 Marc‐André Carbonneau. The network helps show where Marc‐André Carbonneau may publish in the future.
Co-authorship network of co-authors of Marc‐André Carbonneau
This figure shows the co-authorship network connecting the top 25 collaborators of Marc‐André Carbonneau. A scholar is included among the top collaborators of Marc‐André Carbonneau 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 Marc‐André Carbonneau. Marc‐André Carbonneau is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 49 | |
| 5 | 1 | |
| 6 | 8 | |
| 7 | 26 | |
| 8 | 66 | |
| 9 | 4 | |
| 10 | 3 | |
| 11 | 23 | |
| 12 | Multiple instance learning: A survey of problem characteristics and applicationsbreakdown → | 435 |
| 13 | 31 | |
| 14 | 5 | |
| 15 | 38 | |
| 16 | 6 | |
| 17 | 3 | |
| 18 | 15 |
About Marc‐André Carbonneau
Marc‐André Carbonneau is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 18 papers that have together received 715 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (6 papers), Speech and Audio Processing (4 papers) and Video Analysis and Summarization (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (307 citations), Artificial Intelligence (374 citations) and Signal Processing (108 citations). Marc‐André Carbonneau has collaborated with scholars based in Canada, Netherlands and South Africa. Frequent co-authors include Ghyslain Gagnon, Éric Granger, Veronika Cheplygina, Ylva Ferstl, Herman Kamper, Alexandre J. Raymond, Daniel Holden, Nikolaus F. Troje, Jérémie Voix and Tal Arbel. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems and IEEE Signal Processing Letters.
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.