Ignazio Gallo

1.6k total citations · 1 hit paper
81 papers, 882 citations indexed

About

Ignazio Gallo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Ignazio Gallo has authored 81 papers receiving a total of 882 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Computer Vision and Pattern Recognition, 29 papers in Artificial Intelligence and 15 papers in Media Technology. Recurrent topics in Ignazio Gallo's work include Advanced Image and Video Retrieval Techniques (17 papers), Text and Document Classification Technologies (10 papers) and Handwritten Text Recognition Techniques (9 papers). Ignazio Gallo is often cited by papers focused on Advanced Image and Video Retrieval Techniques (17 papers), Text and Document Classification Technologies (10 papers) and Handwritten Text Recognition Techniques (9 papers). Ignazio Gallo collaborates with scholars based in Italy, Pakistan and Austria. Ignazio Gallo's co-authors include Riccardo La Grassa, Nicola Landro, Elisabetta Binaghi, Mirco Boschetti, Shah Nawaz, E. Binaghi, Anwar Ur Rehman, Monica Pepe, Cristina Re and G. Cremonese and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing and Journal of Business Ethics.

In The Last Decade

Ignazio Gallo

72 papers receiving 840 citations

Hit Papers

Deep Object Detection of Crop Weeds: Performance of YOLOv... 2023 2026 2024 2025 2023 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ignazio Gallo Italy 18 386 196 167 120 112 81 882
Yurong Qian China 19 408 1.1× 279 1.4× 310 1.9× 92 0.8× 70 0.6× 115 1.2k
Md Palash Uddin Bangladesh 17 281 0.7× 426 2.2× 404 2.4× 102 0.8× 131 1.2× 82 1.3k
Olivier Strauss France 17 519 1.3× 157 0.8× 58 0.3× 48 0.4× 107 1.0× 82 1.0k
En Fan China 11 236 0.6× 271 1.4× 87 0.5× 36 0.3× 62 0.6× 44 974
Weipeng Jing China 20 256 0.7× 257 1.3× 274 1.6× 100 0.8× 230 2.1× 121 1.1k
Muhammad Jaleed Khan Pakistan 14 419 1.1× 259 1.3× 417 2.5× 98 0.8× 34 0.3× 36 1.2k
Nameirakpam Dhanachandra India 5 404 1.0× 241 1.2× 137 0.8× 29 0.2× 45 0.4× 5 923
Ravi Shankar Singh India 19 201 0.5× 114 0.6× 113 0.7× 37 0.3× 321 2.9× 88 984
Cheng Tan China 12 250 0.6× 325 1.7× 52 0.3× 35 0.3× 70 0.6× 41 1.0k

Countries citing papers authored by Ignazio Gallo

Since Specialization
Citations

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

Fields of papers citing papers by Ignazio Gallo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ignazio Gallo

This figure shows the co-authorship network connecting the top 25 collaborators of Ignazio Gallo. A scholar is included among the top collaborators of Ignazio Gallo 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 Ignazio Gallo. Ignazio Gallo 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.
Rehman, Anwar Ur, et al.. (2025). Improving Classification in Skin Lesion Analysis Through Segmentation. IrInSubria (University of Insubria). 696–703.
2.
Lorenzo, Giuseppe Di, Carlo Buonerba, Antonio Tufano, et al.. (2025). Development and Validation of the PREVESMED Questionnaire: A Comprehensive Tool for Assessing Adherence to a Mediterranean Lifestyle. Complementary Medicine Research. 32(2). 1–13.
4.
Rehman, Anwar Ur & Ignazio Gallo. (2024). Cross-pollination of knowledge for object detection in domain adaptation for industrial automation. International Journal of Intelligent Robotics and Applications. 9(3). 833–851. 1 indexed citations
5.
Candiani, Gabriele, et al.. (2024). Towards crop traits estimation from hyperspectral data: evaluation of neural network models trained with real multi-site data or synthetic RTM simulations. SHILAP Revista de lepidopterología. 39. 475–484. 1 indexed citations
6.
Landro, Nicola, et al.. (2024). Distortion-aware super-resolution for planetary exploration images. IrInSubria (University of Insubria). 300. 21–21. 1 indexed citations
7.
Gallo, Ignazio & Domènec Melé. (2024). Work Integration of People with Mental Disorders Through Social Enterprise: A Humanistic-Personalist Framework and Case Study. Journal of Business Ethics. 199(4). 693–713.
8.
Gallo, Ignazio, Mirco Boschetti, Anwar Ur Rehman, & Gabriele Candiani. (2023). Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images. Remote Sensing. 15(19). 4765–4765. 10 indexed citations
9.
Gallo, Ignazio, et al.. (2023). Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Images. Remote Sensing. 15(2). 539–539. 131 indexed citations breakdown →
10.
Grassa, Riccardo La, G. Cremonese, Ignazio Gallo, Cristina Re, & Elena Martellato. (2023). YOLOLens: A Deep Learning Model Based on Super-Resolution to Enhance the Crater Detection of the Planetary Surfaces. Remote Sensing. 15(5). 1171–1171. 21 indexed citations
11.
Grassa, Riccardo La, Ignazio Gallo, & Nicola Landro. (2022). OCmst: One-class novelty detection using convolutional neural network and minimum spanning trees. IrInSubria (University of Insubria). 4 indexed citations
12.
Gallo, Ignazio, et al.. (2022). Food Recommendations for Reducing Water Footprint. Sustainability. 14(7). 3833–3833. 13 indexed citations
13.
Grassa, Riccardo La, Ignazio Gallo, & Nicola Landro. (2021). Learn class hierarchy using convolutional neural networks. IrInSubria (University of Insubria). 10 indexed citations
14.
Gallo, Ignazio, et al.. (2020). Image and Text fusion for UPMC Food-101 using BERT and CNNs. IrInSubria (University of Insubria). 1–6. 19 indexed citations
15.
Grassa, Riccardo La, Ignazio Gallo, & Nicola Landro. (2020). Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data. IrInSubria (University of Insubria). 2 indexed citations
16.
Grassa, Riccardo La, et al.. (2019). A Classification Methodology Based on Subspace Graphs Learning. BOA (University of Milano-Bicocca). 1 indexed citations
17.
Gallo, Ignazio, et al.. (2011). A Multi-Neural Network Approach to Image Detection and Segmentation of Gas Meter Counter. Machine Vision and Applications. 239–242. 10 indexed citations
18.
Binaghi, Elisabetta, et al.. (2007). An integrated fuzzy logic and web-based framework for active protocol support. International Journal of Medical Informatics. 77(4). 256–271. 13 indexed citations
19.
Raspanti, Mario, Elisabetta Binaghi, Ignazio Gallo, & A. Manelli. (2005). A vision‐based, 3D reconstruction technique for scanning electron microscopy: Direct comparison with atomic force microscopy. Microscopy Research and Technique. 67(1). 1–7. 18 indexed citations
20.
Binaghi, Elisabetta, et al.. (2000). A neural model for fuzzy Dempster–Shafer classifiers. International Journal of Approximate Reasoning. 25(2). 89–121. 32 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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