2,411 research outputs found
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
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
Discontinuity preserving image registration for breathing induced sliding organ motion
Image registration is a powerful tool in medical image analysis and facilitates
the clinical routine in several aspects. It became an indispensable device for
many medical applications including image-guided therapy systems. The
basic goal of image registration is to spatially align two images that show a
similar region of interest. More speci�cally, a displacement �eld respectively
a transformation is estimated, that relates the positions of the pixels or
feature points in one image to the corresponding positions in the other one.
The so gained alignment of the images assists the doctor in comparing and
diagnosing them. There exist di�erent kinds of image registration methods,
those which are capable to estimate a rigid transformation or more generally
an a�ne transformation between the images and those which are able to
capture a more complex motion by estimating a non-rigid transformation.
There are many well established non-rigid registration methods, but those
which are able to preserve discontinuities in the displacement �eld are rather
rare. These discontinuities appear in particular at organ boundaries during
the breathing induced organ motion.
In this thesis, we make use of the idea to combine motion segmentation
with registration to tackle the problem of preserving the discontinuities in
the resulting displacement �eld. We introduce a binary function to represent
the motion segmentation and the proposed discontinuity preserving
non-rigid registration method is then formulated in a variational framework.
Thus, an energy functional is de�ned and its minimisation with respect to
the displacement �eld and the motion segmentation will lead to the desired
result. In theory, one can prove that for the motion segmentation a global
minimiser of the energy functional can be found, if the displacement �eld
is given. The overall minimisation problem, however, is non-convex and a
suitable optimisation strategy has to be considered. Furthermore, depending
on whether we use the pure L1-norm or an approximation of it in the formulation
of the energy functional, we use di�erent numerical methods to solve
the minimisation problem. More speci�cally, when using an approximation
of the L1-norm, the minimisation of the energy functional with respect to the displacement �eld is performed through Brox et al.'s �xed point iteration
scheme, and the minimisation with respect to the motion segmentation
with the dual algorithm of Chambolle. On the other hand, when we make
use of the pure L1-norm in the energy functional, the primal-dual algorithm
of Chambolle and Pock is used for both, the minimisation with respect to
the displacement �eld and the motion segmentation. This approach is clearly
faster compared to the one using the approximation of the L1-norm and also
theoretically more appealing. Finally, to support the registration method
during the minimisation process, we incorporate additionally in a later approach
the information of certain landmark positions into the formulation of
the energy functional, that makes use of the pure L1-norm. Similarly as before,
the primal-dual algorithm of Chambolle and Pock is then used for both,
the minimisation with respect to the displacement �eld and the motion segmentation.
All the proposed non-rigid discontinuity preserving registration
methods delivered promising results for experiments with synthetic images
and real MR images of breathing induced liver motion
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