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Nonlinear inverse model for velocity estimation from an image sequence

Velocity estimation from an image sequence is one of the most challenging inverse problems in computer vision, geosciences, and remote sensing applications. In this paper a nonlinear model has been created for estimating motion field under the constraint of conservation of intensity. A linear differ... Full description

Journal Title: Journal of Geophysical Research: Oceans June 2011, Vol.116(C6), pp.n/a-n/a
Main Author: Chen, Wei
Format: Electronic Article Electronic Article
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ID: ISSN: 0148-0227 ; E-ISSN: 2156-2202 ; DOI: 10.1029/2010JC006924
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recordid: wj10.1029/2010JC006924
title: Nonlinear inverse model for velocity estimation from an image sequence
format: Article
creator:
  • Chen, Wei
subjects:
  • Current
  • Velocity Estimation
  • Image Sequence
  • Thermal Images
  • Tracer Conservation Equation
  • Heat Equation
ispartof: Journal of Geophysical Research: Oceans, June 2011, Vol.116(C6), pp.n/a-n/a
description: Velocity estimation from an image sequence is one of the most challenging inverse problems in computer vision, geosciences, and remote sensing applications. In this paper a nonlinear model has been created for estimating motion field under the constraint of conservation of intensity. A linear differential form of heat or optical flow equation is replaced by a nonlinear temporal integral form of the intensity conservation constraint equation. Iterative equations with Gauss‐Newton and Levenberg‐Marguardt algorithms are formulated based on the nonlinear equations, velocity field modeling, and a nonlinear least squares model. An algorithm with progressive relaxation of the overconstraint to improve the performance of the velocity estimation is also proposed. The new estimator is benchmarked using a numerical simulation model. Both angular and magnitude error measurements based on the synthetic surface heat flow from the numerical model demonstrate that the performance of the new approach with the nonlinear model is much better than the results of using a linear model of heat or optical flow equation. Four sequences of NOAA Advanced Very High Resolution Radiometer (AVHRR) images taken in the New York Bight fields is also used to demonstrate the performance of the nonlinear inverse model, and the estimated velocity fields are compared with those measured with the Coastal Ocean Dynamics Radar array. The experimental results indicate that the nonlinear inverse model provides significant improvement over the linear inverse model for real AVHRR data sets. Velocity estimation is one of the most challenging problems in remote sensing A nonlinear model has been created for estimating motion field Performance is much better than the results of using a linear model
language:
source:
identifier: ISSN: 0148-0227 ; E-ISSN: 2156-2202 ; DOI: 10.1029/2010JC006924
fulltext: fulltext
issn:
  • 0148-0227
  • 01480227
  • 2156-2202
  • 21562202
url: Link


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subjectCurrent ; Velocity Estimation ; Image Sequence ; Thermal Images ; Tracer Conservation Equation ; Heat Equation
descriptionVelocity estimation from an image sequence is one of the most challenging inverse problems in computer vision, geosciences, and remote sensing applications. In this paper a nonlinear model has been created for estimating motion field under the constraint of conservation of intensity. A linear differential form of heat or optical flow equation is replaced by a nonlinear temporal integral form of the intensity conservation constraint equation. Iterative equations with Gauss‐Newton and Levenberg‐Marguardt algorithms are formulated based on the nonlinear equations, velocity field modeling, and a nonlinear least squares model. An algorithm with progressive relaxation of the overconstraint to improve the performance of the velocity estimation is also proposed. The new estimator is benchmarked using a numerical simulation model. Both angular and magnitude error measurements based on the synthetic surface heat flow from the numerical model demonstrate that the performance of the new approach with the nonlinear model is much better than the results of using a linear model of heat or optical flow equation. Four sequences of NOAA Advanced Very High Resolution Radiometer (AVHRR) images taken in the New York Bight fields is also used to demonstrate the performance of the nonlinear inverse model, and the estimated velocity fields are compared with those measured with the Coastal Ocean Dynamics Radar array. The experimental results indicate that the nonlinear inverse model provides significant improvement over the linear inverse model for real AVHRR data sets. Velocity estimation is one of the most challenging problems in remote sensing A nonlinear model has been created for estimating motion field Performance is much better than the results of using a linear model
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titleNonlinear inverse model for velocity estimation from an image sequence
descriptionVelocity estimation from an image sequence is one of the most challenging inverse problems in computer vision, geosciences, and remote sensing applications. In this paper a nonlinear model has been created for estimating motion field under the constraint of conservation of intensity. A linear differential form of heat or optical flow equation is replaced by a nonlinear temporal integral form of the intensity conservation constraint equation. Iterative equations with Gauss‐Newton and Levenberg‐Marguardt algorithms are formulated based on the nonlinear equations, velocity field modeling, and a nonlinear least squares model. An algorithm with progressive relaxation of the overconstraint to improve the performance of the velocity estimation is also proposed. The new estimator is benchmarked using a numerical simulation model. Both angular and magnitude error measurements based on the synthetic surface heat flow from the numerical model demonstrate that the performance of the new approach with the nonlinear model is much better than the results of using a linear model of heat or optical flow equation. Four sequences of NOAA Advanced Very High Resolution Radiometer (AVHRR) images taken in the New York Bight fields is also used to demonstrate the performance of the nonlinear inverse model, and the estimated velocity fields are compared with those measured with the Coastal Ocean Dynamics Radar array. The experimental results indicate that the nonlinear inverse model provides significant improvement over the linear inverse model for real AVHRR data sets. Velocity estimation is one of the most challenging problems in remote sensing A nonlinear model has been created for estimating motion field Performance is much better than the results of using a linear model
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abstractVelocity estimation from an image sequence is one of the most challenging inverse problems in computer vision, geosciences, and remote sensing applications. In this paper a nonlinear model has been created for estimating motion field under the constraint of conservation of intensity. A linear differential form of heat or optical flow equation is replaced by a nonlinear temporal integral form of the intensity conservation constraint equation. Iterative equations with Gauss‐Newton and Levenberg‐Marguardt algorithms are formulated based on the nonlinear equations, velocity field modeling, and a nonlinear least squares model. An algorithm with progressive relaxation of the overconstraint to improve the performance of the velocity estimation is also proposed. The new estimator is benchmarked using a numerical simulation model. Both angular and magnitude error measurements based on the synthetic surface heat flow from the numerical model demonstrate that the performance of the new approach with the nonlinear model is much better than the results of using a linear model of heat or optical flow equation. Four sequences of NOAA Advanced Very High Resolution Radiometer (AVHRR) images taken in the New York Bight fields is also used to demonstrate the performance of the nonlinear inverse model, and the estimated velocity fields are compared with those measured with the Coastal Ocean Dynamics Radar array. The experimental results indicate that the nonlinear inverse model provides significant improvement over the linear inverse model for real AVHRR data sets. Velocity estimation is one of the most challenging problems in remote sensing A nonlinear model has been created for estimating motion field Performance is much better than the results of using a linear model
doi10.1029/2010JC006924
date2011-06