2,814 research outputs found
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
We propose a novel approach for optical flow estimation , targeted at large
displacements with significant oc-clusions. It consists of two steps: i) dense
matching by edge-preserving interpolation from a sparse set of matches; ii)
variational energy minimization initialized with the dense matches. The
sparse-to-dense interpolation relies on an appropriate choice of the distance,
namely an edge-aware geodesic distance. This distance is tailored to handle
occlusions and motion boundaries -- two common and difficult issues for optical
flow computation. We also propose an approximation scheme for the geodesic
distance to allow fast computation without loss of performance. Subsequent to
the dense interpolation step, standard one-level variational energy
minimization is carried out on the dense matches to obtain the final flow
estimation. The proposed approach, called Edge-Preserving Interpolation of
Correspondences (EpicFlow) is fast and robust to large displacements. It
significantly outperforms the state of the art on MPI-Sintel and performs on
par on Kitti and Middlebury
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Morphological interpolation for texture coding
In this paper a new morphological interpolation technique is presented. It is applied to the coding of the smooth (primary) component in a sketch-based image compression approach for very low bit-rates. The interpolation technique is intended to perform two dimensional interpolation from any set of initial pixels and, in particular, from sketch data. It makes intensive use of geodesic dilation, a morphological operator that may be implemented by means of FIFO queues. This results in a very efficient process compared to those that perform interpolation by linear filtering on the initial image. For the application of this method to interpolative image coding, the sketch data is extracted as a set of maximum curvature lines by means of the watershed algorithm. From such information, the interpolation technique obtains a fair reconstruction of both the smooth texture component and the main transitions of the image signal at low bit-rate cost.Peer ReviewedPostprint (published version
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