Belief propagation optical flow for high-resolution image morphing Christian Lipski, Christian Linz, Marcus Magnor

Christian Lipski, Christian Linz, and Marcus Magnor: "Belief propagation optical flow for high-resolution image morphing", August 2010. SIGGRAPH '10: ACM SIGGRAPH 2010 Posters Part of project "Virtual Video Camera". [pdf] [bib]

Abstract

Over the last decade, considerable progress has been made on the
so-called early vision problems. We present an optical flow algorithm
for image morphing that incorporates recent advances in feature
matching, energy minimization, stereo vision and image segmentation.
At the core of our flow estimation we use Efficient Belief
Propagation for energy minimization. While state-of-the-art algorithms
only work on thumbnail-sized images, our novel feature
downsampling scheme in combination with a simple, yet efficient
data term compression can cope with high-resolution data. The
incorporation of SIFT features into data term computation further
resolves matching ambiguities, making long-range flows possible.
We detect occluded areas by evaluating the symmetry of the flow
fields, we further apply Geodesic matting to automatically inpaint
these regions.

TU Braunschweig - Fakultät für Mathematik und Informatik - Computer Graphics - Publications - Belief propagation optical flow for high-resolution image morphing