4,005 research outputs found
Joint Image Reconstruction and Segmentation Using the Potts Model
We propose a new algorithmic approach to the non-smooth and non-convex Potts
problem (also called piecewise-constant Mumford-Shah problem) for inverse
imaging problems. We derive a suitable splitting into specific subproblems that
can all be solved efficiently. Our method does not require a priori knowledge
on the gray levels nor on the number of segments of the reconstruction.
Further, it avoids anisotropic artifacts such as geometric staircasing. We
demonstrate the suitability of our method for joint image reconstruction and
segmentation. We focus on Radon data, where we in particular consider limited
data situations. For instance, our method is able to recover all segments of
the Shepp-Logan phantom from angular views only. We illustrate the
practical applicability on a real PET dataset. As further applications, we
consider spherical Radon data as well as blurred data
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm
This paper addresses the problem of estimating the Potts parameter B jointly
with the unknown parameters of a Bayesian model within a Markov chain Monte
Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem
because performing inference on B requires computing the intractable
normalizing constant of the Potts model. In the proposed MCMC method the
estimation of B is conducted using a likelihood-free Metropolis-Hastings
algorithm. Experimental results obtained for synthetic data show that
estimating B jointly with the other unknown parameters leads to estimation
results that are as good as those obtained with the actual value of B. On the
other hand, assuming that the value of B is known can degrade estimation
performance significantly if this value is incorrect. To illustrate the
interest of this method, the proposed algorithm is successfully applied to real
bidimensional SAR and tridimensional ultrasound images
A Q-Ising model application for linear-time image segmentation
A computational method is presented which efficiently segments digital
grayscale images by directly applying the Q-state Ising (or Potts) model. Since
the Potts model was first proposed in 1952, physicists have studied lattice
models to gain deep insights into magnetism and other disordered systems. For
some time, researchers have realized that digital images may be modeled in much
the same way as these physical systems (i.e., as a square lattice of numerical
values). A major drawback in using Potts model methods for image segmentation
is that, with conventional methods, it processes in exponential time. Advances
have been made via certain approximations to reduce the segmentation process to
power-law time. However, in many applications (such as for sonar imagery),
real-time processing requires much greater efficiency. This article contains a
description of an energy minimization technique that applies four Potts
(Q-Ising) models directly to the image and processes in linear time. The result
is analogous to partitioning the system into regions of four classes of
magnetism. This direct Potts segmentation technique is demonstrated on
photographic, medical, and acoustic images.Comment: 7 pages, 8 figures, revtex, uses subfigure.sty. Central European
Journal of Physics, in press (2010
Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means
This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro-tomography data. We propose a semi-supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three-dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. We conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images
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