Force Estimation and Prediction from Time-Varying Density Images
Author(s)
Ratilal, Purnima; Jagannathan, Srinivasan; Horn, Berthold Klaus Paul; Makris, Nicholas
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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.
Date issued
2011-06Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
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
Other identifiers
INSPEC Accession Number: 11960543
ISSN
0162-8828
2160-9292