Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Handcrafted vs. non-handcrafted features for computer vision classification
2017381 citationsLoris Nanni, Stefano Ghidoni et al.profile →
Local binary patterns variants as texture descriptors for medical image analysis
2010372 citationsLoris Nanni, Alessandra Lumini et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Loris Nanni'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 Loris Nanni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Loris Nanni more than expected).
This network shows the impact of papers produced by Loris Nanni. 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 Loris Nanni. The network helps show where Loris Nanni may publish in the future.
Co-authorship network of co-authors of Loris Nanni
This figure shows the co-authorship network connecting the top 25 collaborators of Loris Nanni.
A scholar is included among the top collaborators of Loris Nanni 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 Loris Nanni. Loris Nanni is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nanni, Loris, et al.. (2019). Research on insect pest image detection and recognition based on bio-inspired methods.. arXiv (Cornell University).1 indexed citations
11.
Nanni, Loris, Alessandra Lumini, & Sheryl Brahnam. (2015). Ensemble of face/eye detectors for accurate automatic face detection. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna).1 indexed citations
12.
Nanni, Loris, Sheryl Brahnam, Stefano Ghidoni, & Emanuele Menegatti. (2014). Region-based approaches and descriptors extracted from the co-occurrence matrix. BearWorks (Missouri State University).5 indexed citations
13.
Nanni, Loris, et al.. (2011). Computer vision for human stem cell derived cardiomyocyte classification: The induced pluripotent vs embryonic stem cell case study. Computing in Cardiology. 569–572.2 indexed citations
14.
Nanni, Loris, Sheryl Brahnam, & Alessandra Lumini. (2010). Selecting the best performing rotation invariant patterns in local binary/ternary patterns. BearWorks (Missouri State University). 369–375.21 indexed citations
15.
Brahnam, Sheryl, Loris Nanni, Jian‐Yu Shi, & Alessandra Lumini. (2010). Local Phase Quantization Texture Descriptor for Protein Classification.. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 159–165.7 indexed citations
16.
Nanni, Loris, Chun‐Nan Hsu, Alessandra Lumini, Yu-Shi Lin, & Chung‐Chih Lin. (2009). Automated cell phenotype image classification combining different methods.. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna).1 indexed citations
17.
Brahnam, Sheryl, Loris Nanni, & Randall S. Sexton. (2008). Neonatal Facial Pain Detection Using NNSOA and LSVM.. BearWorks (Missouri State University). 352–357.17 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.