Evaluating Appearance Models for Recognition, Reacquisition, and Tracking

641 indexed citations
published 2007

Impact in

Classified as

Authors
Douglas A. GrayShane BrennanTao Hai

In The Last Decade

doi.org/w83831177 →

Countries where authors are citing Evaluating Appearance Models for Recognition, Reacquisition, and Tracking

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This map shows the geographic impact of Evaluating Appearance Models for Recognition, Reacquisition, and Tracking. 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 Evaluating Appearance Models for Recognition, Reacquisition, and Tracking with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Evaluating Appearance Models for Recognition, Reacquisition, and Tracking more than expected).

Fields of papers citing Evaluating Appearance Models for Recognition, Reacquisition, and Tracking

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Evaluating Appearance Models for Recognition, Reacquisition, and Tracking. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Evaluating Appearance Models for Recognition, Reacquisition, and Tracking.

About Evaluating Appearance Models for Recognition, Reacquisition, and Tracking

This paper, published in 2007, received 641 indexed citations . Written by Douglas A. Gray, Shane Brennan and Tao Hai covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (634 citations), Biomedical Engineering (281 citations), Artificial Intelligence (42 citations), Safety, Risk, Reliability and Quality (29 citations) and Ocean Engineering (29 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.

This paper is also available at doi.org/w83831177.

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