Computational Visual Media

363 papers and 6.8k indexed citations i.

About

The 363 papers published in Computational Visual Media in the last decades have received a total of 6.8k indexed citations. Papers published in Computational Visual Media usually cover Computer Vision and Pattern Recognition (312 papers), Computer Graphics and Computer-Aided Design (104 papers) and Computational Mechanics (89 papers) specifically the topics of Advanced Vision and Imaging (114 papers), Computer Graphics and Visualization Techniques (99 papers) and 3D Shape Modeling and Analysis (73 papers). The most active scholars publishing in Computational Visual Media are Shi‐Min Hu, Ming‐Ming Cheng, Zheng-Ning Liu, Meng-Hao Guo, Tai‐Jiang Mu, Ralph R. Martin, Deng-Ping Fan, Jun-Xiong Cai, Ling Shao and Song–Hai Zhang.

In The Last Decade

Fields of papers published in Computational Visual Media

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Computational Visual Media. 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 Computational Visual Media.

Countries where authors publish in Computational Visual Media

Since Specialization
Citations

This map shows the geographic impact of research published in Computational Visual Media. 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 Computational Visual Media with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Computational Visual Media 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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2025