Literature DB >> 23547225

Fractional-order variational optical flow model for motion estimation.

Dali Chen1, Hu Sheng, YangQuan Chen, Dingyü Xue.   

Abstract

A new class of fractional-order variational optical flow models, which generalizes the differential of optical flow from integer order to fractional order, is proposed for motion estimation in this paper. The corresponding Euler-Lagrange equations are derived by solving a typical fractional variational problem, and the numerical implementation based on the Grünwald-Letnikov fractional derivative definition is proposed to solve these complicated fractional partial differential equations. Theoretical analysis reveals that the proposed fractional-order variational optical flow model is the generalization of the typical Horn and Schunck (first-order) variational optical flow model and the second-order variational optical flow model, which provides a new idea for us to study the optical flow model and has an important theoretical implication in optical flow model research. The experiments demonstrate the validity of the generalization of differential order.

Year:  2013        PMID: 23547225     DOI: 10.1098/rsta.2012.0148

Source DB:  PubMed          Journal:  Philos Trans A Math Phys Eng Sci        ISSN: 1364-503X            Impact factor:   4.226


  4 in total

1.  Fractional calculus and its applications.

Authors:  Changpin Li; YangQuan Chen; Jürgen Kurths
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2013-04-01       Impact factor: 4.226

2.  Mathematical modelling with experimental validation of viscoelastic properties in non-Newtonian fluids.

Authors:  C M Ionescu; I R Birs; D Copot; C I Muresan; R Caponetto
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-11       Impact factor: 4.226

3.  ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion Estimation.

Authors:  Oliver W Layton
Journal:  Sensors (Basel)       Date:  2021-12-08       Impact factor: 3.576

4.  Dynamic gesture recognition based on 2D convolutional neural network and feature fusion.

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Journal:  Sci Rep       Date:  2022-03-14       Impact factor: 4.379

  4 in total

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