218 research outputs found
NEFI: Network Extraction From Images
Networks and network-like structures are amongst the central building blocks
of many technological and biological systems. Given a mathematical graph
representation of a network, methods from graph theory enable a precise
investigation of its properties. Software for the analysis of graphs is widely
available and has been applied to graphs describing large scale networks such
as social networks, protein-interaction networks, etc. In these applications,
graph acquisition, i.e., the extraction of a mathematical graph from a network,
is relatively simple. However, for many network-like structures, e.g. leaf
venations, slime molds and mud cracks, data collection relies on images where
graph extraction requires domain-specific solutions or even manual. Here we
introduce Network Extraction From Images, NEFI, a software tool that
automatically extracts accurate graphs from images of a wide range of networks
originating in various domains. While there is previous work on graph
extraction from images, theoretical results are fully accessible only to an
expert audience and ready-to-use implementations for non-experts are rarely
available or insufficiently documented. NEFI provides a novel platform allowing
practitioners from many disciplines to easily extract graph representations
from images by supplying flexible tools from image processing, computer vision
and graph theory bundled in a convenient package. Thus, NEFI constitutes a
scalable alternative to tedious and error-prone manual graph extraction and
special purpose tools. We anticipate NEFI to enable the collection of larger
datasets by reducing the time spent on graph extraction. The analysis of these
new datasets may open up the possibility to gain new insights into the
structure and function of various types of networks. NEFI is open source and
available http://nefi.mpi-inf.mpg.de
Randomized algorithms for statistical image analysis and site percolation on square lattices
We propose a novel probabilistic method for detection of objects in noisy
images. The method uses results from percolation and random graph theories. We
present an algorithm that allows to detect objects of unknown shapes in the
presence of random noise. The algorithm has linear complexity and exponential
accuracy and is appropriate for real-time systems. We prove results on
consistency and algorithmic complexity of our procedure.Comment: Submitted for publication on December 11, 200
Robust nonparametric detection of objects in noisy images
We propose a novel statistical hypothesis testing method for detection of
objects in noisy images. The method uses results from percolation theory and
random graph theory. We present an algorithm that allows to detect objects of
unknown shapes in the presence of nonparametric noise of unknown level and of
unknown distribution. No boundary shape constraints are imposed on the object,
only a weak bulk condition for the object's interior is required. The algorithm
has linear complexity and exponential accuracy and is appropriate for real-time
systems. In this paper, we develop further the mathematical formalism of our
method and explore important connections to the mathematical theory of
percolation and statistical physics. We prove results on consistency and
algorithmic complexity of our testing procedure. In addition, we address not
only an asymptotic behavior of the method, but also a finite sample performance
of our test.Comment: This paper initially appeared in 2010 as EURANDOM Report 2010-049.
Link to the abstract at EURANDOM repository:
http://www.eurandom.tue.nl/reports/2010/049-abstract.pdf Link to the paper at
EURANDOM repository: http://www.eurandom.tue.nl/reports/2010/049-report.pd
Stochastic extraction of elongated curvilinear structures with applications
The automatic extraction of elongated curvilinear structures (CLSs) is an important task in various image processing applications, including numerous remote sensing, and biometrical and medical problems. To address this task, we develop a stochastic approach that relies on a fixed-grid, localized Radon transform for line segment extraction and a conditional random field model to incorporate local interactions and refine the extracted CLSs. We propose several different energy data terms, the appropriate choice of which allows us to process images with different noise and geometry properties. The contribution of this paper is the design of a flexible and robust elongated CLS extraction framework that is comparatively fast due to the use of a fixed-grid configuration and fast deterministic Radon-based line detector. We present several different applications of the developed approach, namely: 1) CLS extraction in mammographic images; 2) road networks extraction from optical remotely sensed images; and 3) line extraction from palmprint images. The experimental results demonstrate that the method is fairly robust to CLS curvature and can accurately extract blurred and low-contrast elongated CLS
Multiple testing, uncertainty and realistic pictures
We study statistical detection of grayscale objects in noisy images. The
object of interest is of unknown shape and has an unknown intensity, that can
be varying over the object and can be negative. No boundary shape constraints
are imposed on the object, only a weak bulk condition for the object's interior
is required. We propose an algorithm that can be used to detect grayscale
objects of unknown shapes in the presence of nonparametric noise of unknown
level. Our algorithm is based on a nonparametric multiple testing procedure. We
establish the limit of applicability of our method via an explicit,
closed-form, non-asymptotic and nonparametric consistency bound. This bound is
valid for a wide class of nonparametric noise distributions. We achieve this by
proving an uncertainty principle for percolation on finite lattices.Comment: This paper initially appeared in January 2011 as EURANDOM Report
2011-004. Link to the abstract at EURANDOM Repository:
http://www.eurandom.tue.nl/reports/2011/004-abstract.pdf Link to the paper at
EURANDOM Repository: http://www.eurandom.tue.nl/reports/2011/004-report.pd
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