601 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
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
Shape analysis using fractal dimension: a curvature based approach
The present work shows a novel fractal dimension method for shape analysis.
The proposed technique extracts descriptors from the shape by applying a
multiscale approach to the calculus of the fractal dimension of that shape. The
fractal dimension is obtained by the application of the curvature scale-space
technique to the original shape. Through the application of a multiscale
transform to the dimension calculus, it is obtained a set of numbers
(descriptors) capable of describing with a high precision the shape in
analysis. The obtained descriptors are validated in a classification process.
The results demonstrate that the novel technique provides descriptors highly
reliable, confirming the precision of the proposed method
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
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