Mikhail A. Langovoy
Detection of objects in noisy images based on percolation theory.
Fri, 02/25/2011 - 16:01 — SomNambulAuthors: Mikhail A. Langovoy, Olaf Wittich, Patrick Laurie Davies
Subjects: Statistics
AbstractWe propose a novel statistical 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 nonparametric noise of unknown level. The noise density is assumed
to be unknown and can be very irregular. Our procedure substantially differs
from wavelets-based algorithms. The algorithm has linear complexity and
exponential accuracy and is appropriate for real-time systems.Randomized algorithms for statistical image analysis and site percolation on square lattices.
Fri, 02/25/2011 - 16:01 — SomNambulAuthors: Mikhail A. Langovoy, Olaf Wittich
Subjects: Statistics
AbstractWe 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.Multiple testing, uncertainty and realistic pictures.
Thu, 02/24/2011 - 16:01 — SomNambulAuthors: Mikhail A. Langovoy, Olaf Wittich
Subjects: Probability
AbstractWe 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.Robust nonparametric detection of objects in noisy images.
Thu, 02/24/2011 - 16:01 — SomNambulAuthors: Mikhail A. Langovoy, Olaf Wittich
Subjects: Statistics
AbstractWe 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.Computationally efficient algorithms for statistical image processing. Implementation in R.
Thu, 02/24/2011 - 16:01 — SomNambulAuthors: Mikhail A. Langovoy, Olaf Wittich
Subjects: Computation
AbstractIn the series of our earlier papers on the subject, we proposed 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
developed algorithms that allowed to detect objects of unknown shapes in the
presence of nonparametric noise of unknown level and of unknown distribution.
No boundary shape constraints were imposed on the objects, only a weak bulk
condition for the object's interior was required. Our algorithms have linear
complexity and exponential accuracy.Detection of objects in noisy images and site percolation on square lattices.
Thu, 02/24/2011 - 16:01 — SomNambulAuthors: Mikhail A. Langovoy, Olaf Wittich
Subjects: Statistics
AbstractWe 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. Our procedure substantially differs from
wavelets-based algorithms. 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.
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