Force Estimation and Prediction from Time-Varying Density Images
Name
Jagannathan-2010-Force Estimation and.pdf
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1.55 MB
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Checksum (MD5)
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Author(s)
Ratilal, Purnima
Jagannathan, Srinivasan
Horn, Berthold Klaus Paul
Makris, Nicholas
Date Issued
June 2011
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Jagannathan, S et al. “Force Estimation and Prediction from Time-Varying Density Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 33.6 (2011): 1132-1146. Web. 23 Feb. 2012. © 2011 Institute of Electrical and Electronics Engineers
Version
Final published version
Abstract
We present methods for estimating forces which drive motion observed in density image sequences. Using these forces, we also present methods for predicting velocity and density evolution. To do this, we formulate and apply a Minimum Energy Flow (MEF) method which is capable of estimating both incompressible and compressible flows from time-varying density images. Both the MEF and force-estimation techniques are applied to experimentally obtained density images, spanning spatial scales from micrometers to several kilometers. Using density image sequences describing cell splitting, for example, we show that cell division is driven by gradients in apparent pressure within a cell. Using density image sequences of fish shoals, we also quantify 1) intershoal dynamics such as coalescence of fish groups over tens of kilometers, 2) fish mass flow between different parts of a large shoal, and 3) the stresses acting on large fish shoals.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/tpami.2010.185
