Dense and Deformable Motion Segmentation for Wide Baseline Images

  • Authors:

  • Juho Kannala;Esa Rahtu;Sami S. Brandt;Janne Heikkilä

  • Affiliations:

  • Machine Vision Group, University of Oulu, Finland;Machine Vision Group, University of Oulu, Finland;Machine Vision Group, University of Oulu, Finland;Machine Vision Group, University of Oulu, Finland

  • Venue:
  • SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
  • Year:
  • 2009

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Abstract

In this paper we describe a dense motion segmentation method for wide baseline image pairs. Unlike many previous methods our approach is able to deal with deforming motions and large illumination changes by using a bottom-up segmentation strategy. The method starts from a sparse set of seed matches between the two images and then proceeds to quasi-dense matching which expands the initial seed regions by using local propagation. Then, the quasi-dense matches are grouped into coherently moving segments by using local bending energy as the grouping criterion. The resulting segments are used to initialize the motion layers for the final dense segmentation stage, where the geometric and photometric transformations of the layers are iteratively refined together with the segmentation, which is based on graph cuts. Our approach provides a wider range of applicability than the previous approaches which typically require a rigid planar motion model or motion with small disparity. In addition, we model the photometric transformations in a spatially varying manner. Our experiments demonstrate the performance of the method with real images involving deforming motion and large changes in viewpoint, scale and illumination.