Kalman filter-based algorithms for estimating depth from image sequences
In The Last Decade
doi.org/10.1007/bf00133032 →Countries where authors are citing Kalman filter-based algorithms for estimating depth from image sequences
This map shows the geographic impact of Kalman filter-based algorithms for estimating depth from image sequences. 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 Kalman filter-based algorithms for estimating depth from image sequences with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kalman filter-based algorithms for estimating depth from image sequences more than expected).
Fields of papers citing Kalman filter-based algorithms for estimating depth from image sequences
This network shows the impact of Kalman filter-based algorithms for estimating depth from image sequences. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Kalman filter-based algorithms for estimating depth from image sequences.
About Kalman filter-based algorithms for estimating depth from image sequences
This paper, published in 1989, received 524 indexed citations . Written by Larry Matthies, Takeo Kanade and Richard Szeliski covering the research area of Media Technology and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (465 citations), Aerospace Engineering (261 citations) and Media Technology (144 citations). Published in International Journal of Computer Vision.
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/10.1007/bf00133032.