551 research outputs found
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters
Segmentation of an object from a video is a challenging task in multimedia
applications. Depending on the application, automatic or interactive methods
are desired; however, regardless of the application type, efficient computation
of video object segmentation is crucial for time-critical applications;
specifically, mobile and interactive applications require near real-time
efficiencies. In this paper, we address the problem of video segmentation from
the perspective of efficiency. We initially redefine the problem of video
object segmentation as the propagation of MRF energies along the temporal
domain. For this purpose, a novel and efficient method is proposed to propagate
MRF energies throughout the frames via bilateral filters without using any
global texture, color or shape model. Recently presented bi-exponential filter
is utilized for efficiency, whereas a novel technique is also developed to
dynamically solve graph-cuts for varying, non-lattice graphs in general linear
filtering scenario. These improvements are experimented for both automatic and
interactive video segmentation scenarios. Moreover, in addition to the
efficiency, segmentation quality is also tested both quantitatively and
qualitatively. Indeed, for some challenging examples, significant time
efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014
Grouping Boundary Proposals for Fast Interactive Image Segmentation
Geodesic models are known as an efficient tool for solving various image
segmentation problems. Most of existing approaches only exploit local pointwise
image features to track geodesic paths for delineating the objective
boundaries. However, such a segmentation strategy cannot take into account the
connectivity of the image edge features, increasing the risk of shortcut
problem, especially in the case of complicated scenario. In this work, we
introduce a new image segmentation model based on the minimal geodesic
framework in conjunction with an adaptive cut-based circular optimal path
computation scheme and a graph-based boundary proposals grouping scheme.
Specifically, the adaptive cut can disconnect the image domain such that the
target contours are imposed to pass through this cut only once. The boundary
proposals are comprised of precomputed image edge segments, providing the
connectivity information for our segmentation model. These boundary proposals
are then incorporated into the proposed image segmentation model, such that the
target segmentation contours are made up of a set of selected boundary
proposals and the corresponding geodesic paths linking them. Experimental
results show that the proposed model indeed outperforms state-of-the-art
minimal paths-based image segmentation approaches
Geodesic Models with Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of
finding suitable solutions in various image segmentation scenarios. Existing
geodesic-based segmentation approaches usually exploit image features in
conjunction with geometric regularization terms, such as Euclidean curve length
or curvature-penalized length, for computing geodesic curves. In this paper, we
take into account a more complicated problem: finding curvature-penalized
geodesic paths with a convexity shape prior. We establish new geodesic models
relying on the strategy of orientation-lifting, by which a planar curve can be
mapped to an high-dimensional orientation-dependent space. The convexity shape
prior serves as a constraint for the construction of local geodesic metrics
encoding a particular curvature constraint. Then the geodesic distances and the
corresponding closed geodesic paths in the orientation-lifted space can be
efficiently computed through state-of-the-art Hamiltonian fast marching method.
In addition, we apply the proposed geodesic models to the active contours,
leading to efficient interactive image segmentation algorithms that preserve
the advantages of convexity shape prior and curvature penalization.Comment: This paper has been accepted by TPAM
A Region-based Randers Geodesic Approach for Image Segmentation
The minimal path model based on the Eikonal partial differential equation has
served as a fundamental tool for the applications of image segmentation and
boundary detection in the passed two decades. However, the existing approaches
commonly only exploit the image edge-based features for computing minimal
paths, potentially limiting their performance in complicated segmentation
situations. In this paper, we introduce a new variational image segmentation
model based on the minimal path framework and the eikonal PDE, where the
region-based appearance term that defines then regional homogeneity features
can be taken into account for estimating the associated minimal paths. This is
done by constructing a Randers geodesic metric interpretation to the
region-based active contour energy. As a result, the minimization of the active
contour energy is transformed to finding the solution to the Randers eikonal
PDE.
We also suggest a practical interactive image segmentation strategy, where
the target boundary can be delineated by the concatenation of the piecewise
geodesic paths. We invoke the Finsler variant of the fast marching method to
estimate the geodesic distance map, yielding an efficient implementation of the
proposed Eikonal region-based active contour model. Experimental results on
both synthetic and real images exhibit that our model indeed achieves
encouraging segmentation performance
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