Joint Unsupervised Deformable Spatio-Temporal Alignment of Sequences

L. Zafeiriou, E. Antonakos, S. Zafeiriou, Maja Pantic

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    2 Citations (Scopus)
    28 Downloads (Pure)

    Abstract

    Typically, the problems of spatial and temporal alignment of sequences are considered disjoint. That is, in order to align two sequences, a methodology that (non)-rigidly aligns the images is first applied, followed by temporal alignment of the obtained aligned images. In this paper, we propose the first, to the best of our knowledge, methodology that can jointly spatio-temporally align two sequences, which display highly deformable texture-varying objects. We show that by treating the problems of deformable spatial and temporal alignment jointly, we achieve better results than considering the problems independent. Furthermore, we show that deformable spatio-temporal alignment of faces can be performed in an unsupervised manner (i.e., without employing face trackers or building person-specific deformable models).
    Original languageUndefined
    Title of host publicationProceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR 2016)
    Place of PublicationUSA
    PublisherIEEE Computer Vision and Pattern Recognition
    Pages3382-3390
    Number of pages9
    ISBN (Print)978-1-4673-8852-8
    DOIs
    Publication statusPublished - Jun 2016
    Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, NV, USA, Las Vegas, United States
    Duration: 26 Jun 20161 Jul 2016
    Conference number: 29

    Conference

    Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
    Abbreviated titleCVPR 2016
    CountryUnited States
    CityLas Vegas
    Period26/06/161/07/16
    Other29 June - 1 July 2016

    Keywords

    • deformable spatial and temporal alignment of sequences
    • HMI-HF: Human Factors
    • EWI-27132

    Cite this

    Zafeiriou, L., Antonakos, E., Zafeiriou, S., & Pantic, M. (2016). Joint Unsupervised Deformable Spatio-Temporal Alignment of Sequences. In Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR 2016) (pp. 3382-3390). USA: IEEE Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2016.368