Pietro Perona
About
In The Last Decade
Pietro Perona
255 papers receiving 38.6k citations
Hit Papers
Peers
Comparison fields: 5 of 220
- Computer Vision and Pattern Recognition 27.7k
- Artificial Intelligence 9.0k
- Media Technology 5.1k
- Cognitive Neuroscience 3.4k
- Aerospace Engineering 3.1k
Countries citing papers authored by Pietro Perona
This map shows the geographic impact of Pietro Perona'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 Pietro Perona with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pietro Perona more than expected).
Fields of papers citing papers by Pietro Perona
This network shows the impact of papers produced by Pietro Perona. 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 Pietro Perona. The network helps show where Pietro Perona may publish in the future.
Co-authorship network of co-authors of Pietro Perona
This figure shows the co-authorship network connecting the top 25 collaborators of Pietro Perona. A scholar is included among the top collaborators of Pietro Perona 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 Pietro Perona. Pietro Perona is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 4 | |
| 3 | 5 | |
| 4 | 50 | |
| 5 | 4 | |
| 6 | The iNaturalist Challenge 2017 Dataset | 30 |
| 7 | Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection breakdown → | 311 |
| 8 | 12 | |
| 9 | 17 | |
| 10 | Detecting, Tracking and Classifying Animals in Underwater Video | 5 |
| 11 | Common-Frame Model for Object Recognition | 11 |
| 12 | Self-Tuning Spectral Clustering breakdown → | 1272 |
| 13 | Mutual Boosting for Contextual Inference | 66 |
| 14 | Grouping and dimensionality reduction by locally linear embedding | 86 |
| 15 | Modeling subjective uncertainty in image annotation | 6 |
| 16 | Trainable Cataloging for Digital Image Libraries with Applications to Volcano Detection | 5 |
| 17 | Knowledge discovery in large image databases: dealing with uncertainties in ground truth | 26 |
| 18 | Inferring Ground Truth from Subjective Labelling of Venus Images | 176 |
| 19 | A Current-Mode Position Sensitive Circuit | 1 |
| 20 | 13 |
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.