Matteo Maggioni

1.0k total citations
12 papers, 443 citations indexed

About

Matteo Maggioni is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Matteo Maggioni has authored 12 papers receiving a total of 443 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 4 papers in Media Technology and 3 papers in Artificial Intelligence. Recurrent topics in Matteo Maggioni's work include Image and Signal Denoising Methods (6 papers), Advanced Image Processing Techniques (6 papers) and Advanced Image Fusion Techniques (4 papers). Matteo Maggioni is often cited by papers focused on Image and Signal Denoising Methods (6 papers), Advanced Image Processing Techniques (6 papers) and Advanced Image Fusion Techniques (4 papers). Matteo Maggioni collaborates with scholars based in Sweden, United Kingdom and Finland. Matteo Maggioni's co-authors include Alessandro Foi, Karen Egiazarian, Giacomo Boracchi, J. Alison Noble, Antonietta Pepe, Jussi Tohka, Sylvia Rueda, Aris T. Papageorghiou, Yongxin Yang and Eduardo Pérez-Pellitero and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and The Journal of Urology.

In The Last Decade

Matteo Maggioni

10 papers receiving 428 citations

Peers

Matteo Maggioni

CVPR

MT

CM

BE

RNMI

Salvador Gabarda Spain

Peiyao Wang China

Xiaotong Lu China

Zongxi Song China

Xiaowan Hu China

Ruomei Yan United Kingdom

Ramsin Khoshabeh United States

Tuan Q. Pham Netherlands

Weihong Guo United States

Salvador Gabarda Spain

Matteo Maggioni

301 ×0.9

CVPR

216 ×1.3

MT

17 ×0.3

CM

30 ×0.5

BE

40 ×1.2

RNMI

Citations per year, relative to Matteo Maggioni Matteo Maggioni (= 1×) peers Salvador Gabarda

Countries citing papers authored by Matteo Maggioni

Since Specialization

Citations

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

Fields of papers citing papers by Matteo Maggioni

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matteo Maggioni

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

All Works

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12 of 12 papers shown

1.

Deng, Jiankang, et al.. (2023). Linear Complexity Self-Attention with $3^{\text{rd}}$ Order Polynomials. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(11). 1–12. 4 indexed citations

2.

Rocco, Bernardo, Filippo Turri, Mattia Sangalli, et al.. (2023). V06-04 ROBOTIC-ASSISTED INTRACORPOREAL NEOBLADDER RECONSTRUCTION WITH Y-SHAPED RESERVOIR: VIDEO DESCRIPTION AND CLINICAL OUTCOMES. The Journal of Urology. 209(Supplement 4).

3.

Maggioni, Matteo, et al.. (2023). Tunable Convolutions with Parametric Multi-Loss Optimization. 20226–20236. 1 indexed citations

4.

Tanay, Thomas, et al.. (2023). Factorized Dynamic Fully-Connected Layers for Neural Networks. 1366–1375. 2 indexed citations

5.

Tanay, Thomas, Aleš Leonardis, & Matteo Maggioni. (2023). Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations. 20898–20907.

6.

Dong, Nanqing, Matteo Maggioni, Yongxin Yang, et al.. (2022). Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 2930–2936. 4 indexed citations

7.

Foi, Alessandro, Matteo Maggioni, Antonietta Pepe, et al.. (2014). Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation. Computerized Medical Imaging and Graphics. 38(8). 774–784. 22 indexed citations

8.

Maggioni, Matteo, et al.. (2014). Structural texture similarity metric based on intra-class variances. 2. 1992–1996. 3 indexed citations

9.

Maggioni, Matteo, et al.. (2014). Joint Removal of Random and Fixed-Pattern Noise Through Spatiotemporal Video Filtering. IEEE Transactions on Image Processing. 23(10). 4282–4296. 63 indexed citations

10.

Maggioni, Matteo, Giacomo Boracchi, Alessandro Foi, & Karen Egiazarian. (2012). Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms. IEEE Transactions on Image Processing. 21(9). 3952–3966. 258 indexed citations

11.

Maggioni, Matteo & Alessandro Foi. (2012). Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 38 indexed citations

12.

Maggioni, Matteo, Giacomo Boracchi, Alessandro Foi, & Karen Egiazarian. (2011). Video denoising using separable 4D nonlocal spatiotemporal transforms. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7870. 787003–787003. 48 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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