Pattern Recognition Letters

8.3k papers and 205.8k indexed citations i.

About

The 8.3k papers published in Pattern Recognition Letters in the last decades have received a total of 205.8k indexed citations. Papers published in Pattern Recognition Letters usually cover Computer Vision and Pattern Recognition (5.8k papers), Artificial Intelligence (2.8k papers) and Signal Processing (1.2k papers) specifically the topics of Image Retrieval and Classification Techniques (1.3k papers), Advanced Image and Video Retrieval Techniques (1.2k papers) and Face and Expression Recognition (960 papers). The most active scholars publishing in Pattern Recognition Letters are Tom Fawcett, Anil K. Jain, Robert P. W. Duin, Josef Kittler, Pavel Pudil, Jana Novovičová, Horst Bunke, Azriel Rosenfeld, Witold Pedrycz and Alexander Toet.

In The Last Decade

Fields of papers published in Pattern Recognition Letters

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Pattern Recognition Letters. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Pattern Recognition Letters.

Countries where authors publish in Pattern Recognition Letters

Since Specialization
Citations

This map shows the geographic impact of research published in Pattern Recognition Letters. 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 papers published in Pattern Recognition Letters with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pattern Recognition Letters more than expected).

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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