7,882 research outputs found
Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes
In this article, a new method for segmentation and restoration of images on
two-dimensional surfaces is given. Active contour models for image segmentation
are extended to images on surfaces. The evolving curves on the surfaces are
mathematically described using a parametric approach. For image restoration, a
diffusion equation with Neumann boundary conditions is solved in a
postprocessing step in the individual regions. Numerical schemes are presented
which allow to efficiently compute segmentations and denoised versions of
images on surfaces. Also topology changes of the evolving curves are detected
and performed using a fast sub-routine. Finally, several experiments are
presented where the developed methods are applied on different artificial and
real images defined on different surfaces
Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes
In this paper, we introduce a novel parametric method for segmentation of
three-dimensional images. We consider a piecewise constant version of the
Mumford-Shah and the Chan-Vese functionals and perform a region-based
segmentation of 3D image data. An evolution law is derived from energy
minimization problems which push the surfaces to the boundaries of 3D objects
in the image. We propose a parametric scheme which describes the evolution of
parametric surfaces. An efficient finite element scheme is proposed for a
numerical approximation of the evolution equations. Since standard parametric
methods cannot handle topology changes automatically, an efficient method is
presented to detect, identify and perform changes in the topology of the
surfaces. One main focus of this paper are the algorithmic details to handle
topology changes like splitting and merging of surfaces and change of the genus
of a surface. Different artificial images are studied to demonstrate the
ability to detect the different types of topology changes. Finally, the
parametric method is applied to segmentation of medical 3D images
Contour evolution scheme for variational image segmentation and smoothing
An algorithm, based on the MumfordâShah (MâS) functional, for image contour segmentation and object smoothing in the presence of noise is proposed. However, in the proposed algorithm, contour length minimisation is not required and it is demonstrated that the MâS functional without contour length minimisation becomes an edge detector. Optimisation of this nonlinear functional is based on the method of calculus of variations, which is implemented by using the level set method. Fourier and Legendreâs series are also employed to improve the segmentation performance of the proposed algorithm. The segmentation results clearly demonstrate the effectiveness of the proposed approach for images with low signal-to-noise ratios
Joint Optical Flow and Temporally Consistent Semantic Segmentation
The importance and demands of visual scene understanding have been steadily
increasing along with the active development of autonomous systems.
Consequently, there has been a large amount of research dedicated to semantic
segmentation and dense motion estimation. In this paper, we propose a method
for jointly estimating optical flow and temporally consistent semantic
segmentation, which closely connects these two problem domains and leverages
each other. Semantic segmentation provides information on plausible physical
motion to its associated pixels, and accurate pixel-level temporal
correspondences enhance the accuracy of semantic segmentation in the temporal
domain. We demonstrate the benefits of our approach on the KITTI benchmark,
where we observe performance gains for flow and segmentation. We achieve
state-of-the-art optical flow results, and outperform all published algorithms
by a large margin on challenging, but crucial dynamic objects.Comment: 14 pages, Accepted for CVRSUAD workshop at ECCV 201
Semantically Guided Depth Upsampling
We present a novel method for accurate and efficient up- sampling of sparse
depth data, guided by high-resolution imagery. Our approach goes beyond the use
of intensity cues only and additionally exploits object boundary cues through
structured edge detection and semantic scene labeling for guidance. Both cues
are combined within a geodesic distance measure that allows for
boundary-preserving depth in- terpolation while utilizing local context. We
model the observed scene structure by locally planar elements and formulate the
upsampling task as a global energy minimization problem. Our method determines
glob- ally consistent solutions and preserves fine details and sharp depth
bound- aries. In our experiments on several public datasets at different levels
of application, we demonstrate superior performance of our approach over the
state-of-the-art, even for very sparse measurements.Comment: German Conference on Pattern Recognition 2016 (Oral
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