Computer Graphics
TU Braunschweig

High Resolution Image Correspondences for Video Post-Production


High Resolution Image Correspondences for Video Post-Production

We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction and baseline adjustment. We

incorporate recent advances in feature matching, energy minimization, stereo vision and data clustering into our approach. At the core of our correspondence 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 correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically inpaint these regions.


Author(s):Christian Lipski, Christian Linz, Thomas Neumann, Markus Wacker, Marcus Magnor
Year:2010
Month:November
Type:Article in conference proceedings
Book:Proc. European Conference on Visual Media Production (CVMP) (IEEE Computer Society)
Presented at:European Conference on Visual Media Production (CVMP)
Note:http://doi.ieeecomputersociety.org/10.1109/CVMP.2010.12
Project(s): Image-space Editing of 3D Content  Virtual Video Camera 


@inproceedings{Lipski10hires,
  title = {High Resolution Image Correspondences for Video Post-Production},
  author = {Lipski, Christian and Linz, Christian and Neumann, Thomas and Wacker, Markus and Magnor, Marcus},
  booktitle = {Proc. European Conference on Visual Media Production ({CVMP})},
  volume = {7},
  note = {http://doi.ieeecomputersociety.org/10.1109/{CVMP}.2010.12},
  pages = {33--39},
  month = {Nov},
  year = {2010}
}

Authors