2,134 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
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
The purposes of this study were to investigate: 1) the effect of placement of
region-of-interest (ROI) for texture analysis of subchondral bone in knee
radiographs, and 2) the ability of several texture descriptors to distinguish
between the knees with and without radiographic osteoarthritis (OA). Bilateral
posterior-anterior knee radiographs were analyzed from the baseline of OAI and
MOST datasets. A fully automatic method to locate the most informative region
from subchondral bone using adaptive segmentation was developed. We used an
oversegmentation strategy for partitioning knee images into the compact regions
that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick
features, Shannon entropy, and HOG methods were computed within the standard
ROI and within the proposed adaptive ROIs. Subsequently, we built logistic
regression models to identify and compare the performances of each texture
descriptor and each ROI placement method using 5-fold cross validation setting.
Importantly, we also investigated the generalizability of our approach by
training the models on OAI and testing them on MOST dataset.We used area under
the receiver operating characteristic (ROC) curve (AUC) and average precision
(AP) obtained from the precision-recall (PR) curve to compare the results. We
found that the adaptive ROI improves the classification performance (OA vs.
non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC).
We also observed that, from all texture parameters, LBP yielded the best
performance in all settings with the best AUC of 0.840 [0.825, 0.852] and
associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art
approaches, our results suggest that the proposed adaptive ROI approach in
texture analysis of subchondral bone can increase the diagnostic performance
for detecting the presence of radiographic OA
Texture descriptor combining fractal dimension and artificial crawlers
Texture is an important visual attribute used to describe images. There are
many methods available for texture analysis. However, they do not capture the
details richness of the image surface. In this paper, we propose a new method
to describe textures using the artificial crawler model. This model assumes
that each agent can interact with the environment and each other. Since this
swarm system alone does not achieve a good discrimination, we developed a new
method to increase the discriminatory power of artificial crawlers, together
with the fractal dimension theory. Here, we estimated the fractal dimension by
the Bouligand-Minkowski method due to its precision in quantifying structural
properties of images. We validate our method on two texture datasets and the
experimental results reveal that our method leads to highly discriminative
textural features. The results indicate that our method can be used in
different texture applications.Comment: 12 pages 9 figures. Paper in press: Physica A: Statistical Mechanics
and its Application
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
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 Model of Plant Identification System Using GLCM, Lacunarity And Shen Features
Recently, many approaches have been introduced by several researchers to
identify plants. Now, applications of texture, shape, color and vein features
are common practices. However, there are many possibilities of methods can be
developed to improve the performance of such identification systems. Therefore,
several experiments had been conducted in this research. As a result, a new
novel approach by using combination of Gray-Level Co-occurrence Matrix,
lacunarity and Shen features and a Bayesian classifier gives a better result
compared to other plant identification systems. For comparison, this research
used two kinds of several datasets that were usually used for testing the
performance of each plant identification system. The results show that the
system gives an accuracy rate of 97.19% when using the Flavia dataset and
95.00% when using the Foliage dataset and outperforms other approaches.Comment: 10 page
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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