19,172 research outputs found
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
In this paper, we propose a novel geometric model fitting method, called
Mode-Seeking on Hypergraphs (MSH),to deal with multi-structure data even in the
presence of severe outliers. The proposed method formulates geometric model
fitting as a mode seeking problem on a hypergraph in which vertices represent
model hypotheses and hyperedges denote data points. MSH intuitively detects
model instances by a simple and effective mode seeking algorithm. In addition
to the mode seeking algorithm, MSH includes a similarity measure between
vertices on the hypergraph and a weight-aware sampling technique. The proposed
method not only alleviates sensitivity to the data distribution, but also is
scalable to large scale problems. Experimental results further demonstrate that
the proposed method has significant superiority over the state-of-the-art
fitting methods on both synthetic data and real images.Comment: Proceedings of the IEEE International Conference on Computer Vision,
pp. 2902-2910, 201
Cell nuclei detection using globally optimal active contours with shape prior
Cell nuclei detection in fluorescent microscopic images is an important and time consuming task for a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make this a challenging task for automated detection of individual nuclei using image analysis. This paper proposes a novel and robust detection method based on the active contour framework. The method exploits prior knowledge of the nucleus shape in order to better detect individual nuclei. The method is formulated as the optimization of a convex energy function. The proposed method shows accurate detection results even for clusters of nuclei where state of the art methods fail
WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network
Deep learning has driven a great progress in natural and biological image
processing. However, in material science and engineering, there are often some
flaws and indistinctions in material microscopic images induced from complex
sample preparation, even due to the material itself, hindering the detection of
target objects. In this work, we propose WPU-net that redesigns the
architecture and weighted loss of U-Net, which forces the network to integrate
information from adjacent slices and pays more attention to the topology in
boundary detection task. Then, the WPU-net is applied into a typical material
example, i.e., the grain boundary detection of polycrystalline material.
Experiments demonstrate that the proposed method achieves promising performance
and outperforms state-of-the-art methods. Besides, we propose a new method for
object tracking between adjacent slices, which can effectively reconstruct 3D
structure of the whole material. Finally, we present a material microscopic
image dataset with the goal of advancing the state-of-the-art in image
processing for material science.Comment: technical repor
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
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