Michela Lecca
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Media Technology top 5%
- Atomic and Molecular Physics, and Optics
- Cognitive Neuroscience
- Co-authors
- Alessandro RizziRaul SerapioniGabriele GianiniM. GottardiStefano MesselodiCarla Maria ModenaElisabetta FarellaManuele Rusci
- Topics
- Image Enhancement Techniques (25 papers)Color Science and Applications (14 papers)CCD and CMOS Imaging Sensors (12 papers)
- Cited by
- Computer Vision and Pattern RecognitionMedia TechnologyAtomic and Molecular Physics, and Optics
- Partner nations
- ItalyUnited StatesSwitzerland
In The Last Decade
Michela Lecca
49 papers receiving 401 citations
Peers
Comparison fields: 5 of 63
- Computer Vision and Pattern Recognition 307
- Electrical and Electronic Engineering 92
- Media Technology 89
- Atomic and Molecular Physics, and Optics 70
- Cognitive Neuroscience 43
Countries citing papers authored by Michela Lecca
This map shows the geographic impact of Michela Lecca'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 Michela Lecca with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michela Lecca more than expected).
Fields of papers citing papers by Michela Lecca
This network shows the impact of papers produced by Michela Lecca. 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 Michela Lecca. The network helps show where Michela Lecca may publish in the future.
Co-authorship network of co-authors of Michela Lecca
This figure shows the co-authorship network connecting the top 25 collaborators of Michela Lecca. A scholar is included among the top collaborators of Michela Lecca 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 Michela Lecca. Michela Lecca is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 6 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 4 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 11 | |
| 11 | 11 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 17 | |
| 15 | 1 | |
| 16 | 24 | |
| 17 | 36 | |
| 18 | 20 | |
| 19 | 15 | |
| 20 | 3 |
About Michela Lecca
Michela Lecca is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Space and Planetary Science, having authored 52 papers that have together received 412 indexed citations. Recurring topics across this work include Image Enhancement Techniques (25 papers), Color Science and Applications (14 papers) and CCD and CMOS Imaging Sensors (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (307 citations), Media Technology (89 citations) and Atomic and Molecular Physics, and Optics (70 citations). Michela Lecca has collaborated with scholars based in Italy, United States and Switzerland. Frequent co-authors include Alessandro Rizzi, Raul Serapioni, Gabriele Gianini, M. Gottardi, Stefano Messelodi, Carla Maria Modena, Elisabetta Farella, Manuele Rusci, Davide Rossi and Luca Benini. Their work appears in journals such as IEEE Transactions on Image Processing, IEEE Access and IEEE Journal of Solid-State Circuits.
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.