Spatio-temporal alignment of pedobarographic image sequences.

This article presents a methodology to align plantar pressure image sequences simultaneously in time and space. The spatial position and orientation of a foot in a sequence are changed to match the foot represented in a second sequence. Simultaneously with the spatial alignment, the temporal scale o...

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Published in:Medical & Biological Engineering & Computing Vol. 49; no. 7; pp. 843 - 851
Main Authors: Oliveira FP, Sousa A, Santos R, Tavares JM, Oliveira, Francisco P M, Sousa, Andreia, Santos, Rubim, Tavares, João Manuel R S
Format: research Journal Article
Published: Springer Nature Jul2011
Online Access:View this record in EBSCOhost
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      dt: Jul2011
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      pub: Springer Nature
      place: New York, New York
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        atl: Spatio-temporal alignment of pedobarographic image sequences.
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        au:
          Oliveira FP
          Sousa A
          Santos R
          Tavares JM
          Oliveira, Francisco P M
          Sousa, Andreia
          Santos, Rubim
          Tavares, João Manuel R S
        affil: Faculdade de Engenharia da Universidade do Porto (FEUP)/Instituto de Engenharia Mecânica e Gestão Industrial (INEGI), Rua Dr. Roberto Frias, 4200-465, Porto, Portugal
      sug:
        subj:
          Foot Physiology
          Image Interpretation, Computer Assisted Methods
          Walking Physiology
          Adolescence
          Algorithms
          Biomechanics
          Female
          Male
          Pressure
          Quality Assurance
          Young Adult
          Adolescent: 13-18 years
          Female
          Male
      ab: This article presents a methodology to align plantar pressure image sequences simultaneously in time and space. The spatial position and orientation of a foot in a sequence are changed to match the foot represented in a second sequence. Simultaneously with the spatial alignment, the temporal scale of the first sequence is transformed with the aim of synchronizing the two input footsteps. Consequently, the spatial correspondence of the foot regions along the sequences as well as the temporal synchronizing is automatically attained, making the study easier and more straightforward. In terms of spatial alignment, the methodology can use one of four possible geometric transformation models: rigid, similarity, affine, or projective. In the temporal alignment, a polynomial transformation up to the 4th degree can be adopted in order to model linear and curved time behaviors. Suitable geometric and temporal transformations are found by minimizing the mean squared error (MSE) between the input sequences. The methodology was tested on a set of real image sequences acquired from a common pedobarographic device. When used in experimental cases generated by applying geometric and temporal control transformations, the methodology revealed high accuracy. In addition, the intra-subject alignment tests from real plantar pressure image sequences showed that the curved temporal models produced better MSE results (P < 0.001) than the linear temporal model. This article represents an important step forward in the alignment of pedobarographic image data, since previous methods can only be applied on static images.
      pubtype: Academic Journal
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        Journal Article
      ougenre: Article
    language: English
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