21 research outputs found
Model-based learning of local image features for unsupervised texture segmentation
Features that capture well the textural patterns of a certain class of images
are crucial for the performance of texture segmentation methods. The manual
selection of features or designing new ones can be a tedious task. Therefore,
it is desirable to automatically adapt the features to a certain image or class
of images. Typically, this requires a large set of training images with similar
textures and ground truth segmentation. In this work, we propose a framework to
learn features for texture segmentation when no such training data is
available. The cost function for our learning process is constructed to match a
commonly used segmentation model, the piecewise constant Mumford-Shah model.
This means that the features are learned such that they provide an
approximately piecewise constant feature image with a small jump set. Based on
this idea, we develop a two-stage algorithm which first learns suitable
convolutional features and then performs a segmentation. We note that the
features can be learned from a small set of images, from a single image, or
even from image patches. The proposed method achieves a competitive rank in the
Prague texture segmentation benchmark, and it is effective for segmenting
histological images
Exact algorithms for -TV regularization of real-valued or circle-valued signals
We consider -TV regularization of univariate signals with values on the
real line or on the unit circle. While the real data space leads to a convex
optimization problem, the problem is non-convex for circle-valued data. In this
paper, we derive exact algorithms for both data spaces. A key ingredient is the
reduction of the infinite search spaces to a finite set of configurations,
which can be scanned by the Viterbi algorithm. To reduce the computational
complexity of the involved tabulations, we extend the technique of distance
transforms to non-uniform grids and to the circular data space. In total, the
proposed algorithms have complexity where is the length
of the signal and is the number of different values in the data set. In
particular, the complexity is for quantized data. It is the
first exact algorithm for TV regularization with circle-valued data, and it is
competitive with the state-of-the-art methods for scalar data, assuming that
the latter are quantized
Total variation regularization for manifold-valued data
We consider total variation minimization for manifold valued data. We propose
a cyclic proximal point algorithm and a parallel proximal point algorithm to
minimize TV functionals with -type data terms in the manifold case.
These algorithms are based on iterative geodesic averaging which makes them
easily applicable to a large class of data manifolds. As an application, we
consider denoising images which take their values in a manifold. We apply our
algorithms to diffusion tensor images, interferometric SAR images as well as
sphere and cylinder valued images. For the class of Cartan-Hadamard manifolds
(which includes the data space in diffusion tensor imaging) we show the
convergence of the proposed TV minimizing algorithms to a global minimizer
Disparity and Optical Flow Partitioning Using Extended Potts Priors
This paper addresses the problems of disparity and optical flow partitioning
based on the brightness invariance assumption. We investigate new variational
approaches to these problems with Potts priors and possibly box constraints.
For the optical flow partitioning, our model includes vector-valued data and an
adapted Potts regularizer. Using the notation of asymptotically level stable
functions we prove the existence of global minimizers of our functionals. We
propose a modified alternating direction method of minimizers. This iterative
algorithm requires the computation of global minimizers of classical univariate
Potts problems which can be done efficiently by dynamic programming. We prove
that the algorithm converges both for the constrained and unconstrained
problems. Numerical examples demonstrate the very good performance of our
partitioning method
The L1-Potts functional for robust jump-sparse reconstruction
We investigate the non-smooth and non-convex -Potts functional in
discrete and continuous time. We show -convergence of discrete
-Potts functionals towards their continuous counterpart and obtain a
convergence statement for the corresponding minimizers as the discretization
gets finer. For the discrete -Potts problem, we introduce an time
and space algorithm to compute an exact minimizer. We apply -Potts
minimization to the problem of recovering piecewise constant signals from noisy
measurements It turns out that the -Potts functional has a quite
interesting blind deconvolution property. In fact, we show that mildly blurred
jump-sparse signals are reconstructed by minimizing the -Potts functional.
Furthermore, for strongly blurred signals and known blurring operator, we
derive an iterative reconstruction algorithm