Multiple objects tracking in fluorescence microscopy
Journal of Mathematical Biology, ISSN: 0303-6812, Vol: 58, Issue: 1-2, Page: 57-80
2009
- 40Citations
- 84Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations40
- Citation Indexes40
- 40
- CrossRef30
- Captures84
- Readers84
- 84
Article Description
Many processes in cell biology are connected to the movement of compact entities: intracellular vesicles and even single molecules. The tracking of individual objects is important for understanding cellular dynamics. Here we describe the tracking algorithms which have been developed in the non-biological fields and successfully applied to object detection and tracking in biological applications. The characteristics features of the different algorithms are compared.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=63249094432&origin=inward; http://dx.doi.org/10.1007/s00285-008-0180-4; http://www.ncbi.nlm.nih.gov/pubmed/18478231; http://link.springer.com/10.1007/s00285-008-0180-4; http://www.springerlink.com/index/10.1007/s00285-008-0180-4; http://www.springerlink.com/index/pdf/10.1007/s00285-008-0180-4; https://dx.doi.org/10.1007/s00285-008-0180-4; https://link.springer.com/article/10.1007/s00285-008-0180-4
Springer Science and Business Media LLC
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