Fŕed́eric Precioso

2.5k total citations
58 papers, 549 citations indexed

About

Fŕed́eric Precioso is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Fŕed́eric Precioso has authored 58 papers receiving a total of 549 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Computer Vision and Pattern Recognition, 17 papers in Artificial Intelligence and 11 papers in Signal Processing. Recurrent topics in Fŕed́eric Precioso's work include Advanced Image and Video Retrieval Techniques (15 papers), Image Retrieval and Classification Techniques (14 papers) and Video Analysis and Summarization (7 papers). Fŕed́eric Precioso is often cited by papers focused on Advanced Image and Video Retrieval Techniques (15 papers), Image Retrieval and Classification Techniques (14 papers) and Video Analysis and Summarization (7 papers). Fŕed́eric Precioso collaborates with scholars based in France, Canada and United Kingdom. Fŕed́eric Precioso's co-authors include Matthieu Cord, Michel Barlaud, Mohamed Limam, Michaël Unser, Thierry Blu, Arnaud Droit, Lucile Sassatelli, Isabelle Fournier, Michel Salzet and Charles Bouveyron and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Fŕed́eric Precioso

54 papers receiving 524 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fŕed́eric Precioso France 14 308 145 69 48 38 58 549
Jianhua Chen China 10 98 0.3× 118 0.8× 59 0.9× 20 0.4× 42 1.1× 57 321
Syed Ali Abbas Pakistan 9 365 1.2× 208 1.4× 28 0.4× 80 1.7× 32 0.8× 28 674
Jishang Wei United States 12 198 0.6× 215 1.5× 54 0.8× 32 0.7× 8 0.2× 26 438
Eric Garcia United States 8 213 0.7× 171 1.2× 52 0.8× 21 0.4× 32 0.8× 13 442
Alaa Eleyan Türkiye 12 366 1.2× 88 0.6× 95 1.4× 8 0.2× 44 1.2× 56 595
René Alquézar Spain 14 287 0.9× 225 1.6× 58 0.8× 37 0.8× 12 0.3× 46 484
István Dénes Germany 6 99 0.3× 148 1.0× 57 0.8× 37 0.8× 41 1.1× 12 454
Sraban Kumar Mohanty India 14 138 0.4× 290 2.0× 54 0.8× 38 0.8× 21 0.6× 33 442
Shengfeng Pan China 5 117 0.4× 231 1.6× 90 1.3× 69 1.4× 21 0.6× 5 539
Kyungim Baek United States 8 312 1.0× 71 0.5× 193 2.8× 43 0.9× 21 0.6× 18 507

Countries citing papers authored by Fŕed́eric Precioso

Since Specialization
Citations

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

Fields of papers citing papers by Fŕed́eric Precioso

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fŕed́eric Precioso

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

All Works

20 of 20 papers shown
1.
Tastet, Lionel, Mickaël Leclercq, Fŕed́eric Precioso, et al.. (2024). AI-Enhanced Prediction of Aortic Stenosis Progression. JACC Advances. 3(10). 101234–101234. 3 indexed citations
2.
Wu, Hui-Yin, et al.. (2024). Visual Objectification in Films: Towards a New AI Task for Video Interpretation. SPIRE - Sciences Po Institutional REpository. 10864–10874. 1 indexed citations
3.
Bouveyron, Charles, et al.. (2024). Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea. Ecological Informatics. 85. 102959–102959. 1 indexed citations
4.
Leclercq, Mickaël, Florence Roux‐Dalvai, Shannon Leslie, et al.. (2024). BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks. Nature Communications. 15(1). 3777–3777. 6 indexed citations
5.
Precioso, Fŕed́eric, et al.. (2023). Early Diagnosis: End-to-End CNN–LSTM Models for Mass Spectrometry Data Classification. Analytical Chemistry. 95(36). 13431–13437. 13 indexed citations
6.
Blay–Fornarino, Mireille, et al.. (2023). Taming the Diversity of Computational Notebooks. SPIRE - Sciences Po Institutional REpository. 27–33.
7.
Pasquier, Nicolas, et al.. (2021). Semi-supervised consensus clustering based on closed patterns. Knowledge-Based Systems. 235. 107599–107599. 14 indexed citations
8.
Saudemont, Philippe, Fŕed́eric Precioso, Nina Ogrinc, et al.. (2020). Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification. Nature Communications. 11(1). 5595–5595. 55 indexed citations
9.
Pasquier, Nicolas, et al.. (2020). Semi-Supervised Consensus Clustering Based on Frequent Closed Itemsets. HAL (Le Centre pour la Communication Scientifique Directe).
10.
Mayaffre, Damon, et al.. (2020). Du texte à l’intertexte. Le palimpseste Macron au révélateur de l’Intelligence artificielle. SHILAP Revista de lepidopterología. 78. 6003–6003. 3 indexed citations
11.
Limam, Mohamed & Fŕed́eric Precioso. (2017). AF Detection and ECG Classification�based on Convolutional Recurrent Neural Network. Computing in cardiology. 44. 3 indexed citations
12.
Ducoffe, Mélanie & Fŕed́eric Precioso. (2017). Active learning strategy for CNN combining batchwise Dropout and Query-By-Committee.. The European Symposium on Artificial Neural Networks. 1 indexed citations
13.
Lingrand, Diane, et al.. (2014). Plant Species Recognition using Bag-Of-Word with SVM Classifier in the Context of the LifeCLEF Challenge.. CLEF (Working Notes). 738–746. 2 indexed citations
14.
Lingrand, Diane, et al.. (2013). SIFT, BoW Architecture and one-against-all Support Vector Machine.. CLEF (Working Notes). 2 indexed citations
15.
Cord, Matthieu, et al.. (2011). Locality-Sensitive Hashing for Chi2 Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(2). 402–409. 49 indexed citations
16.
Devaux, Alexandre, et al.. (2009). Face Blurring for Privacy in Street-level Geoviewers Combining Face, Body and Skin Detectors. Machine Vision and Applications. 86–89. 3 indexed citations
17.
Cámara-Chávez, Guillermo, Fŕed́eric Precioso, Matthieu Cord, Sylvie Philipp‐Foliguet, & Arnaldo de Albuquerque Araújo. (2006). SHOT BOUNDARY DETECTION AT TRECVID 2006. TRECVID. 8 indexed citations
18.
Precioso, Fŕed́eric, Michel Barlaud, Thierry Blu, & Michaël Unser. (2005). Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm. IEEE Transactions on Image Processing. 14(7). 910–924. 40 indexed citations
19.
Jehan‐Besson, Stéphanie, et al.. (2004). From Snakes to Region-Based Active Contours Defined by Region-Dependent Parameters. Applied Optics. 43(2). 247–247. 2 indexed citations
20.
Precioso, Fŕed́eric & Michel Barlaud. (2002). B-Spline Active Contour with Handling of Topology Changes for Fast Video Segmentation. SHILAP Revista de lepidopterología. 2 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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