A framework for estimating potential fluid flow from digital imagery Public Deposited

http://ir.library.oregonstate.edu/concern/defaults/pv63g186n

This is the publisher’s final pdf. The published article is copyrighted by AIP Publishing LLC and can be found at:  http://www.aip.org/. Copyright Statement Basic Permissions Limited license IS GRANTED to individuals accessing this document and its component documents and/or files for the following personal, noncommercial uses: 1. Retrieving or printing a copy of any document or file mounted on this server 2. Establishing a link or links to any document or file mounted on this server Individuals accessing this document and its component documents and/or files are NOT GRANTED license to: 1. Alter a copy of any retrieved or printed document or file from this server 2. Distribute a copy (electronic or otherwise) of any document or file from this server without permission from the American Institute of Physics (direct requests to  Web_Management@aip.org 3. Charge for a copy (electronic or otherwise) of any document or file from this server This server and its contents, unless otherwise indicated, are the property of the American Institute of Physics

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Given image data of a fluid flow, the flow field, < u, v >, governing the evolution of the system can be estimated using a variational approach to optical flow. Assuming that the flow field governing the advection is the symplectic gradient of a stream function or the gradient of a potential function-both falling under the category of a potential flow-it is natural to re-frame the optical flow problem to reconstruct the stream or potential function directly rather than the components of the flow individually. There are several advantages to this framework. Minimizing a functional based on the stream or potential function rather than based on the components of the flow will ensure that the computed flow is a potential flow. Next, this approach allows a more natural method for imposing scientific priors on the computed flow, via regularization of the optical flow functional. Also, this paradigm shift gives a framework-rather than an algorithm-and can be applied to nearly any existing variational optical flow technique. In this work, we develop the mathematical formulation of the potential optical flow framework and demonstrate the technique on synthetic flows that represent important dynamics for mass transport in fluid flows, as well as a flow generated by a satellite data-verified ocean model of temperature transport. (C) 2013 AIP Publishing LLC.
Resource Type
DOI
Date Available
Date Issued
Citation
  • Luttman, A., Bollt, E. M., Basnayake, R., Kramer, S., & Tufillaro, N. B. (2013). A framework for estimating potential fluid flow from digital imagery. Chaos (Woodbury, N.Y.), 23(3), 033134. doi:10.1063/1.4821188
Series
Keyword
Rights Statement
Funding Statement (additional comments about funding)
Publisher
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Submitted by Deborah Campbell (deborah.campbell@oregonstate.edu) on 2013-11-14T18:24:33Z No. of bitstreams: 1 TufillaroNicholasBCEOASFrameworkEstimatingPotential.pdf: 5358448 bytes, checksum: 7263e9006ce690a8f6e3d32c9b1aa00d (MD5)
  • description.provenance : Made available in DSpace on 2013-11-14T18:24:33Z (GMT). No. of bitstreams: 1 TufillaroNicholasBCEOASFrameworkEstimatingPotential.pdf: 5358448 bytes, checksum: 7263e9006ce690a8f6e3d32c9b1aa00d (MD5) Previous issue date: 2013-09-11

Relationships

Parents:

This work has no parents.

Last modified

Downloadable Content

Download PDF

Items