128,942 research outputs found
Image Characterization and Classification by Physical Complexity
We present a method for estimating the complexity of an image based on
Bennett's concept of logical depth. Bennett identified logical depth as the
appropriate measure of organized complexity, and hence as being better suited
to the evaluation of the complexity of objects in the physical world. Its use
results in a different, and in some sense a finer characterization than is
obtained through the application of the concept of Kolmogorov complexity alone.
We use this measure to classify images by their information content. The method
provides a means for classifying and evaluating the complexity of objects by
way of their visual representations. To the authors' knowledge, the method and
application inspired by the concept of logical depth presented herein are being
proposed and implemented for the first time.Comment: 30 pages, 21 figure
Characterizing neuromorphologic alterations with additive shape functionals
The complexity of a neuronal cell shape is known to be related to its
function. Specifically, among other indicators, a decreased complexity in the
dendritic trees of cortical pyramidal neurons has been associated with mental
retardation. In this paper we develop a procedure to address the
characterization of morphological changes induced in cultured neurons by
over-expressing a gene involved in mental retardation. Measures associated with
the multiscale connectivity, an additive image functional, are found to give a
reasonable separation criterion between two categories of cells. One category
consists of a control group and two transfected groups of neurons, and the
other, a class of cat ganglionary cells. The reported framework also identified
a trend towards lower complexity in one of the transfected groups. Such results
establish the suggested measures as an effective descriptors of cell shape
History of art paintings through the lens of entropy and complexity
Art is the ultimate expression of human creativity that is deeply influenced
by the philosophy and culture of the corresponding historical epoch. The
quantitative analysis of art is therefore essential for better understanding
human cultural evolution. Here we present a large-scale quantitative analysis
of almost 140 thousand paintings, spanning nearly a millennium of art history.
Based on the local spatial patterns in the images of these paintings, we
estimate the permutation entropy and the statistical complexity of each
painting. These measures map the degree of visual order of artworks into a
scale of order-disorder and simplicity-complexity that locally reflects
qualitative categories proposed by art historians. The dynamical behavior of
these measures reveals a clear temporal evolution of art, marked by transitions
that agree with the main historical periods of art. Our research shows that
different artistic styles have a distinct average degree of entropy and
complexity, thus allowing a hierarchical organization and clustering of styles
according to these metrics. We have further verified that the identified groups
correspond well with the textual content used to qualitatively describe the
styles, and that the employed complexity-entropy measures can be used for an
effective classification of artworks.Comment: 10 two-column pages, 5 figures; accepted for publication in PNAS
[supplementary information available at
http://www.pnas.org/highwire/filestream/824089/field_highwire_adjunct_files/0/pnas.1800083115.sapp.pdf
DNA sequences classification and computation scheme based on the symmetry principle
The DNA sequences containing multifarious novel symmetrical structure frequently play crucial role in how genomes work. Here we present a new scheme for understanding the structural features and potential mathematical rules of symmetrical DNA sequences using a method containing stepwise classification and recursive computation. By defining the symmetry of DNA sequences, we classify all sequences and conclude a series of recursive equations for computing the quantity of all classes of sequences existing theoretically; moreover, the symmetries of the typical sequences at different levels are analyzed. The classification and quantitative relation demonstrate that DNA sequences have recursive and nested properties. The scheme may help us better discuss the formation and the growth mechanism of DNA sequences because it has a capability of educing the information about structure and quantity of longer sequences according to that of shorter sequences by some recursive rules. Our scheme may provide a new stepping stone to the theoretical characterization, as well as structural analysis, of DNA sequences
Multilayer Complex Network Descriptors for Color-Texture Characterization
A new method based on complex networks is proposed for color-texture
analysis. The proposal consists on modeling the image as a multilayer complex
network where each color channel is a layer, and each pixel (in each color
channel) is represented as a network vertex. The network dynamic evolution is
accessed using a set of modeling parameters (radii and thresholds), and new
characterization techniques are introduced to capt information regarding within
and between color channel spatial interaction. An automatic and adaptive
approach for threshold selection is also proposed. We conduct classification
experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT.
Results among various literature methods are compared, including deep
convolutional neural networks with pre-trained architectures. The proposed
method presented the highest overall performance over the 5 datasets, with 97.7
of mean accuracy against 97.0 achieved by the ResNet convolutional neural
network with 50 layers.Comment: 20 pages, 7 figures and 4 table
A Survey of Cellular Automata: Types, Dynamics, Non-uniformity and Applications
Cellular automata (CAs) are dynamical systems which exhibit complex global
behavior from simple local interaction and computation. Since the inception of
cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention
of several researchers over various backgrounds and fields for modelling
different physical, natural as well as real-life phenomena. Classically, CAs
are uniform. However, non-uniformity has also been introduced in update
pattern, lattice structure, neighborhood dependency and local rule. In this
survey, we tour to the various types of CAs introduced till date, the different
characterization tools, the global behaviors of CAs, like universality,
reversibility, dynamics etc. Special attention is given to non-uniformity in
CAs and especially to non-uniform elementary CAs, which have been very useful
in solving several real-life problems.Comment: 43 pages; Under review in Natural Computin
Image-based deep learning for classification of noise transients in gravitational wave detectors
The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.Comment: 25 pages, 8 figures, accepted for publication in Classical and
Quantum Gravit
Characterization of nanostructured material images using fractal descriptors
This work presents a methodology to the morphology analysis and
characterization of nanostructured material images acquired from FEG-SEM (Field
Emission Gun-Scanning Electron Microscopy) technique. The metrics were
extracted from the image texture (mathematical surface) by the volumetric
fractal descriptors, a methodology based on the Bouligand-Minkowski fractal
dimension, which considers the properties of the Minkowski dilation of the
surface points. An experiment with galvanostatic anodic titanium oxide samples
prepared in oxalyc acid solution using different conditions of applied current,
oxalyc acid concentration and solution temperature was performed. The results
demonstrate that the approach is capable of characterizing complex morphology
characteristics such as those present in the anodic titanium oxide.Comment: 8 pages, 5 figures, accepted for publication Physica
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