Romaric Audigier

557 total citations
22 papers, 188 citations indexed

About

Romaric Audigier is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Romaric Audigier has authored 22 papers receiving a total of 188 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 3 papers in Biomedical Engineering. Recurrent topics in Romaric Audigier's work include Medical Image Segmentation Techniques (8 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Anomaly Detection Techniques and Applications (7 papers). Romaric Audigier is often cited by papers focused on Medical Image Segmentation Techniques (8 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Anomaly Detection Techniques and Applications (7 papers). Romaric Audigier collaborates with scholars based in France and Brazil. Romaric Audigier's co-authors include Roberto Lotufo, Alexandre X. Falcão, Frédéric Lerasle, Jaonary Rabarisoa, Stéphane Canu, Sébastien Ambellouis, Samia Aïnouz, Amaury Habrard, Hervé Le Borgne and Florian Chabot and has published in prestigious journals such as IEEE Access, Image and Vision Computing and Computer Vision and Image Understanding.

In The Last Decade

Romaric Audigier

22 papers receiving 180 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Romaric Audigier France	8	155	80	14	14	11	22	188
Gurram Sunitha India	6	74 0.5×	56 0.7×	9 0.6×	21 1.5×	16 1.5×	26	196
Chaim Baskin Israel	6	118 0.8×	85 1.1×	5 0.4×	5 0.4×	7 0.6×	13	182
Dileep Kumar Yadav India	8	124 0.8×	29 0.4×	8 0.6×	17 1.2×	21 1.9×	29	175
Shell Xu Hu United Kingdom	6	86 0.6×	125 1.6×	7 0.5×	4 0.3×	16 1.5×	9	186
Yuren Cong Germany	4	162 1.0×	95 1.2×	5 0.4×	11 0.8×	5 0.5×	9	211
Xinzhe Li China	7	114 0.7×	140 1.8×	15 1.1×	5 0.4×	8 0.7×	20	203
Yan Ke China	10	287 1.9×	61 0.8×	9 0.6×	9 0.6×	29 2.6×	34	370

Countries citing papers authored by Romaric Audigier

Since Specialization

Citations

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

Fields of papers citing papers by Romaric Audigier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Romaric Audigier

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

All Works

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20 of 20 papers shown

Habrard, Amaury, et al.. (2023). Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient?. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 75–84. 3 indexed citations

Chabot, Florian, et al.. (2023). Generalized Pseudo-Labeling in Consistency Regularization for Semi-Supervised Learning. SPIRE - Sciences Po Institutional REpository. 525–529. 1 indexed citations

Audigier, Romaric, et al.. (2023). Cumulative unsupervised multi-domain adaptation for Holstein cattle re-identification. Artificial Intelligence in Agriculture. 10. 46–60. 6 indexed citations

Audigier, Romaric, et al.. (2022). Object-Centric and Memory-Guided Normality Reconstruction for Video Anomaly Detection. 2022 IEEE International Conference on Image Processing (ICIP). 2691–2695. 5 indexed citations

Audigier, Romaric, et al.. (2021). Optimal Transport as a Defense Against Adversarial Attacks. arXiv (Cornell University). 5044–5051. 5 indexed citations

Audigier, Romaric, et al.. (2021). Improving Unsupervised Domain Adaptive Re-Identification Via Source-Guided Selection of Pseudo-Labeling Hyperparameters. IEEE Access. 9. 149780–149795. 5 indexed citations

Audigier, Romaric, et al.. (2021). A Formal Approach to Good Practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification. SSRN Electronic Journal. 1 indexed citations

Audigier, Romaric, et al.. (2021). Detecting Human-to-Human-or-Object (H²O) Interactions with DIABOLO. arXiv (Cornell University). 32. 1–8. 1 indexed citations

Audigier, Romaric, et al.. (2021). Describe Me If You Can! Characterized Instance-Level Human Parsing. arXiv (Cornell University). 16. 2528–2532. 7 indexed citations

10.

Rabarisoa, Jaonary, et al.. (2019). End-To-End Person Search Sequentially Trained On Aggregated Dataset. arXiv (Cornell University). 4574–4578. 6 indexed citations

11.

Audigier, Romaric, et al.. (2015). RIMOC, a feature to discriminate unstructured motions: Application to violence detection for video-surveillance. Computer Vision and Image Understanding. 144. 121–143. 44 indexed citations

12.

Audigier, Romaric, et al.. (2015). Embedded security system for multi-modal surveillance in a railway carriage. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9652. 96520C–96520C. 5 indexed citations

13.

Audigier, Romaric, et al.. (2015). Online multi-person tracking based on global sparse collaborative representations. 33. 2414–2418. 12 indexed citations

14.

Audigier, Romaric & Roberto Lotufo. (2009). Relationships between some watershed definitions and their tie-zone transforms. Image and Vision Computing. 28(10). 1472–1482. 6 indexed citations

15.

Audigier, Romaric & Roberto Lotufo. (2007). Watershed by image foresting transform, tie-zone, and theoretical relationships with other watershed definitions.. Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research). 277–288. 18 indexed citations

16.

Audigier, Romaric & Roberto Lotufo. (2007). Seed-Relative Segmentation Robustness of Watershed and Fuzzy Connectedness Approaches. 61–70. 14 indexed citations

17.

Audigier, Romaric & Roberto Lotufo. (2007). Uniquely-Determined Thinning of the Tie-Zone Watershed Based on Label Frequency. Journal of Mathematical Imaging and Vision. 27(2). 157–173. 7 indexed citations

18.

Audigier, Romaric, Roberto Lotufo, & Alexandre X. Falcão. (2006). 3D visualization to assist iterative object definition from medical images. Computerized Medical Imaging and Graphics. 30(4). 217–230. 6 indexed citations

19.

Audigier, Romaric & Roberto Lotufo. (2005). Tie-Zone Watershed, Bottlenecks, and Segmentation Robustness Analysis. 55–62. 5 indexed citations

20.

Audigier, Romaric, Roberto Lotufo, & Alexandre X. Falcão. (2004). On integrating iterative segmentation by watershed with tridimensional visualization of MRIs. 37. 130–137. 3 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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