74,583 research outputs found
Two novel evolutionary formulations of the graph coloring problem
We introduce two novel evolutionary formulations of the problem of coloring
the nodes of a graph. The first formulation is based on the relationship that
exists between a graph's chromatic number and its acyclic orientations. It
views such orientations as individuals and evolves them with the aid of
evolutionary operators that are very heavily based on the structure of the
graph and its acyclic orientations. The second formulation, unlike the first
one, does not tackle one graph at a time, but rather aims at evolving a
`program' to color all graphs belonging to a class whose members all have the
same number of nodes and other common attributes. The heuristics that result
from these formulations have been tested on some of the Second DIMACS
Implementation Challenge benchmark graphs, and have been found to be
competitive when compared to the several other heuristics that have also been
tested on those graphs.Comment: To appear in Journal of Combinatorial Optimizatio
Multi-resolution texture classification based on local image orientation
The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases
MRI image segmantation based on edge detection
Cílem této práce je představit základní segmentační techniky používáné v oblasti medicínského zpracování obrazových dat a pomocí 3D prohlížeče schopného zobrazit 3D obrazy implementovat segmentační modul založený na hranové detekci a vyhodnotit výsledky. Navrhovaný prohlížeč je sestavený v prostředi Matlab GUI a je schopen načíst objem 3D snímků představující lidskou hlavu. Navrhovaný segmentační modul je založen na použití hranových detektorů, zejména Cannyho detektoru.The aim of this thesis is to present the basic segmentation techniques uses in the field of medical image processing and by using a 3D viewer able to visualize 3D images, implement a segmentation module based on edges detection and evaluate the results. The proposed viewer is a 3D viewer build using matlab GUI and is able to load a volume of images representing the human head. The proposed segmentation module is based on the use of edge detectors particularly the Canny algorithm.
Improving Texture Categorization with Biologically Inspired Filtering
Within the domain of texture classification, a lot of effort has been spent
on local descriptors, leading to many powerful algorithms. However,
preprocessing techniques have received much less attention despite their
important potential for improving the overall classification performance. We
address this question by proposing a novel, simple, yet very powerful
biologically-inspired filtering (BF) which simulates the performance of human
retina. In the proposed approach, given a texture image, after applying a DoG
filter to detect the "edges", we first split the filtered image into two "maps"
alongside the sides of its edges. The feature extraction step is then carried
out on the two "maps" instead of the input image. Our algorithm has several
advantages such as simplicity, robustness to illumination and noise, and
discriminative power. Experimental results on three large texture databases
show that with an extremely low computational cost, the proposed method
improves significantly the performance of many texture classification systems,
notably in noisy environments. The source codes of the proposed algorithm can
be downloaded from https://sites.google.com/site/nsonvu/code.Comment: 11 page
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and
analysis of perception systems, especially those based on machine learning.
Specifically, we consider the problems of training a perception system to
handle rare events, testing its performance under different conditions, and
debugging failures. We show how a probabilistic programming language can help
address these problems by specifying distributions encoding interesting types
of inputs and sampling these to generate specialized training and test sets.
More generally, such languages can be used for cyber-physical systems and
robotics to write environment models, an essential prerequisite to any formal
analysis. In this paper, we focus on systems like autonomous cars and robots,
whose environment is a "scene", a configuration of physical objects and agents.
We design a domain-specific language, Scenic, for describing "scenarios" that
are distributions over scenes. As a probabilistic programming language, Scenic
allows assigning distributions to features of the scene, as well as
declaratively imposing hard and soft constraints over the scene. We develop
specialized techniques for sampling from the resulting distribution, taking
advantage of the structure provided by Scenic's domain-specific syntax.
Finally, we apply Scenic in a case study on a convolutional neural network
designed to detect cars in road images, improving its performance beyond that
achieved by state-of-the-art synthetic data generation methods.Comment: 41 pages, 36 figures. Full version of a PLDI 2019 paper (extending UC
Berkeley EECS Department Tech Report No. UCB/EECS-2018-8
Topological quantum memory
We analyze surface codes, the topological quantum error-correcting codes
introduced by Kitaev. In these codes, qubits are arranged in a two-dimensional
array on a surface of nontrivial topology, and encoded quantum operations are
associated with nontrivial homology cycles of the surface. We formulate
protocols for error recovery, and study the efficacy of these protocols. An
order-disorder phase transition occurs in this system at a nonzero critical
value of the error rate; if the error rate is below the critical value (the
accuracy threshold), encoded information can be protected arbitrarily well in
the limit of a large code block. This phase transition can be accurately
modeled by a three-dimensional Z_2 lattice gauge theory with quenched disorder.
We estimate the accuracy threshold, assuming that all quantum gates are local,
that qubits can be measured rapidly, and that polynomial-size classical
computations can be executed instantaneously. We also devise a robust recovery
procedure that does not require measurement or fast classical processing;
however for this procedure the quantum gates are local only if the qubits are
arranged in four or more spatial dimensions. We discuss procedures for
encoding, measurement, and performing fault-tolerant universal quantum
computation with surface codes, and argue that these codes provide a promising
framework for quantum computing architectures.Comment: 39 pages, 21 figures, REVTe
Evaluation of 3D gradient filters for estimation of the surface orientation in CTC
The extraction of the gradient information from 3D surfaces plays an important role for many applications including 3D graphics and medical imaging. The extraction of the 3D gradient information is performed by filtering the input data with high pass filters that are typically implemented using 3×3×3 masks. Since these filters extract the
gradient information in small neighborhood, the estimated gradient information will be very sensitive to image noise. The development of a 3D gradient operator that is robust
to image noise is particularly important since the medical datasets are characterized by a relatively low signal to noise ratio. The aim of this paper is to detail the
implementation of an optimized 3D gradient operator that is applied to sample the local curvature of the colon wall in CT data and its influence on the overall performance of
our CAD-CTC method. The developed 3D gradient operator has been applied to extract the local curvature of the colon wall in a large number CT datasets captured with different radiation doses and the experimental results are presented and discussed
An error estimate of Gaussian Recursive Filter in 3Dvar problem
Computational kernel of the three-dimensional variational data assimilation
(3D-Var) problem is a linear system, generally solved by means of an iterative
method. The most costly part of each iterative step is a matrix-vector product
with a very large covariance matrix having Gaussian correlation structure. This
operation may be interpreted as a Gaussian convolution, that is a very
expensive numerical kernel. Recursive Filters (RFs) are a well known way to
approximate the Gaussian convolution and are intensively applied in the
meteorology, in the oceanography and in forecast models. In this paper, we deal
with an oceanographic 3D-Var data assimilation scheme, named OceanVar, where
the linear system is solved by using the Conjugate Gradient (GC) method by
replacing, at each step, the Gaussian convolution with RFs. Here we give
theoretical issues on the discrete convolution approximation with a first order
(1st-RF) and a third order (3rd-RF) recursive filters. Numerical experiments
confirm given error bounds and show the benefits, in terms of accuracy and
performance, of the 3-rd RF.Comment: 9 page
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