883 research outputs found
Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis
The present work proposes the development of a novel method to provide
descriptors for colored texture images. The method consists in two steps. In
the first, we apply a linear transform in the color space of the image aiming
at highlighting spatial structuring relations among the color of pixels. In a
second moment, we apply a multiscale approach to the calculus of fractal
dimension based on Fourier transform. From this multiscale operation, we
extract the descriptors used to discriminate the texture represented in digital
images. The accuracy of the method is verified in the classification of two
color texture datasets, by comparing the performance of the proposed technique
to other classical and state-of-the-art methods for color texture analysis. The
results showed an advantage of almost 3% of the proposed technique over the
second best approach.Comment: Chaos, Volume 21, Issue 4, 201
Fractal descriptors based on the probability dimension: a texture analysis and classification approach
In this work, we propose a novel technique for obtaining descriptors of
gray-level texture images. The descriptors are provided by applying a
multiscale transform to the fractal dimension of the image estimated through
the probability (Voss) method. The effectiveness of the descriptors is verified
in a classification task using benchmark over texture datasets. The results
obtained demonstrate the efficiency of the proposed method as a tool for the
description and discrimination of texture images.Comment: 7 pages, 6 figures. arXiv admin note: text overlap with
arXiv:1205.282
Texture analysis by multi-resolution fractal descriptors
This work proposes a texture descriptor based on fractal theory. The method
is based on the Bouligand-Minkowski descriptors. We decompose the original
image recursively into 4 equal parts. In each recursion step, we estimate the
average and the deviation of the Bouligand-Minkowski descriptors computed over
each part. Thus, we extract entropy features from both average and deviation.
The proposed descriptors are provided by the concatenation of such measures.
The method is tested in a classification experiment under well known datasets,
that is, Brodatz and Vistex. The results demonstrate that the proposed
technique achieves better results than classical and state-of-the-art texture
descriptors, such as Gabor-wavelets and co-occurrence matrix.Comment: 8 pages, 6 figure
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
Multiscale Fractal Descriptors Applied to Nanoscale Images
This work proposes the application of fractal descriptors to the analysis of
nanoscale materials under different experimental conditions. We obtain
descriptors for images from the sample applying a multiscale transform to the
calculation of fractal dimension of a surface map of such image. Particularly,
we have used the}Bouligand-Minkowski fractal dimension. We applied these
descriptors to discriminate between two titanium oxide films prepared under
different experimental conditions. Results demonstrate the discrimination power
of proposed descriptors in such kind of application
Brachiaria species identification using imaging techniques based on fractal descriptors
The use of a rapid and accurate method in diagnosis and classification of
species and/or cultivars of forage has practical relevance, scientific and
trade in various areas of study. Thus, leaf samples of fodder plant species
\textit{Brachiaria} were previously identified, collected and scanned to be
treated by means of artificial vision to make the database and be used in
subsequent classifications. Forage crops used were: \textit{Brachiaria
decumbens} cv. IPEAN; \textit{Brachiaria ruziziensis} Germain \& Evrard;
\textit{Brachiaria Brizantha} (Hochst. ex. A. Rich.) Stapf; \textit{Brachiaria
arrecta} (Hack.) Stent. and \textit{Brachiaria spp}. The images were analyzed
by the fractal descriptors method, where a set of measures are obtained from
the values of the fractal dimension at different scales. Therefore such values
are used as inputs for a state-of-the-art classifier, the Support Vector
Machine, which finally discriminates the images according to the respective
species.Comment: 7 pages, 5 figure
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