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Force Estimation and Prediction from Time-Varying Density Images
June 2011 (vol. 33 no. 6)
pp. 1132-1146
Srinivasan Jagannathan, Massachusetts Institute of Technology, Cambridge
Berthold Klaus Paul Horn, Massachusetts Institute of Technology, Cambridge
Purnima Ratilal, Northeastern University, Boston
Nicholas Constantine Makris, Massachusetts Institute of Technology, Cambridge
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.

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Index Terms:
Force estimation, density prediction, compressible flow estimation, minimum energy flow.
Citation:
Srinivasan Jagannathan, Berthold Klaus Paul Horn, Purnima Ratilal, Nicholas Constantine Makris, "Force Estimation and Prediction from Time-Varying Density Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1132-1146, June 2011, doi:10.1109/TPAMI.2010.185
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