Statistical Estimation of Fluid Flow: An Image Restoration Approach

  • Konstantia Moirogiorgou
  • Michalis Zervakis
  • Andreas E. Savakis
  • Ioannis Sibetheros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8887)


This paper focuses on Fluid Motion-Field Estimation from video data, which is a useful but challenging problem in environmental monitoring. Rivers are often monitored by flashy hydrographs that exhibit characteristic response times ranging from minutes to hours. In order to estimate the river discharge during a flush flood event, the temporary motion vector field of the river surface is needed. This paper presents a new approach in statistical estimation of fluid flow that calculates a local flow probability distribution function in the frequency domain. Our work improves upon the inefficiencies of spatial estimation of the auto-regressive STAR model and converts motion estimation into a restoration problem, where the local field can be computed fast in the frequency domain, while various natural constraints can be taken into account within the inversion strategy of the motion estimation process.


River Flow Motion Vector Local Neighborhood Search Window Motion Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Konstantia Moirogiorgou
    • 1
  • Michalis Zervakis
    • 1
  • Andreas E. Savakis
    • 2
  • Ioannis Sibetheros
    • 3
  1. 1.TUC/Electronic and Computer EngineeringUniversity CampusChaniaGreece
  2. 2.Department of Computer EngineeringRochester Institute of TechnologyNew YorkUSA
  3. 3.Department of Civil Engineering and Surveying & Geoinformatics EngineeringTEI AthensAigaleoGreece

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