5,054 research outputs found
Describing Textures in the Wild
Patterns and textures are defining characteristics of many natural objects: a
shirt can be striped, the wings of a butterfly can be veined, and the skin of
an animal can be scaly. Aiming at supporting this analytical dimension in image
understanding, we address the challenging problem of describing textures with
semantic attributes. We identify a rich vocabulary of forty-seven texture terms
and use them to describe a large dataset of patterns collected in the wild.The
resulting Describable Textures Dataset (DTD) is the basis to seek for the best
texture representation for recognizing describable texture attributes in
images. We port from object recognition to texture recognition the Improved
Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized
texture descriptors not only on our problem, but also in established material
recognition datasets. We also show that the describable attributes are
excellent texture descriptors, transferring between datasets and tasks; in
particular, combined with IFV, they significantly outperform the
state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks.
We also demonstrate that they produce intuitive descriptions of materials and
Internet images.Comment: 13 pages; 12 figures Fixed misplaced affiliatio
Deep filter banks for texture recognition, description, and segmentation
Visual textures have played a key role in image understanding because they
convey important semantics of images, and because texture representations that
pool local image descriptors in an orderless manner have had a tremendous
impact in diverse applications. In this paper we make several contributions to
texture understanding. First, instead of focusing on texture instance and
material category recognition, we propose a human-interpretable vocabulary of
texture attributes to describe common texture patterns, complemented by a new
describable texture dataset for benchmarking. Second, we look at the problem of
recognizing materials and texture attributes in realistic imaging conditions,
including when textures appear in clutter, developing corresponding benchmarks
on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic
texture representations, including bag-of-visual-words and the Fisher vectors,
in the context of deep learning and show that these have excellent efficiency
and generalization properties if the convolutional layers of a deep model are
used as filter banks. We obtain in this manner state-of-the-art performance in
numerous datasets well beyond textures, an efficient method to apply deep
features to image regions, as well as benefit in transferring features from one
domain to another.Comment: 29 pages; 13 figures; 8 table
High-Precision Localization Using Ground Texture
Location-aware applications play an increasingly critical role in everyday
life. However, satellite-based localization (e.g., GPS) has limited accuracy
and can be unusable in dense urban areas and indoors. We introduce an
image-based global localization system that is accurate to a few millimeters
and performs reliable localization both indoors and outside. The key idea is to
capture and index distinctive local keypoints in ground textures. This is based
on the observation that ground textures including wood, carpet, tile, concrete,
and asphalt may look random and homogeneous, but all contain cracks, scratches,
or unique arrangements of fibers. These imperfections are persistent, and can
serve as local features. Our system incorporates a downward-facing camera to
capture the fine texture of the ground, together with an image processing
pipeline that locates the captured texture patch in a compact database
constructed offline. We demonstrate the capability of our system to robustly,
accurately, and quickly locate test images on various types of outdoor and
indoor ground surfaces
Systematic Parameter Space Search of Extended Quark-Lepton Complementarity
We systematically investigate the parameter space of neutrino and charged
lepton mass matrices for textures motivated by an extended quark-lepton
complementarity. As the basic hypothesis, we postulate that all mixing angles
in U_l and U_nu be either maximal or described by powers of a single small
quantity epsilon ~ theta_C. All mass hierarchies are described by this epsilon
as well. In this study, we do not assume specific forms for U_l and U_nu, such
as large mixing coming from the neutrino sector only. We perform a systematic
scan of the 262,144 generated mixing matrices for being compatible with current
experimental data, and find a sample of 2,468 possibilities. We then analyze
and classify the effective charged lepton and neutrino mass textures, where we
especially focus on a subset of models getting under pressure for small
theta_13. In addition, we predict the mixing angle distributions from our
sample of all valid textures, and study the robustness of this prediction. We
also demonstrate how our procedure can be extended to predictions of the Dirac
and Majorana phases in U_PMNS. For instance, we find that CP conservation in
neutrino oscillations is preferred, and we can impose a lower bound on the
mixing matrix element for neutrinoless double beta decay.Comment: 37 pages, 9 figures. Small error in Eq. (36) corrected, and Figs. 8
and 9 updated. Final version matched to Nuclear Physics B version. A detailed
list of our textures compatible with current data can be obtained at
http://theorie.physik.uni-wuerzburg.de/~winter/Resources/Textures/index.htm
A Generative Model of Natural Texture Surrogates
Natural images can be viewed as patchworks of different textures, where the
local image statistics is roughly stationary within a small neighborhood but
otherwise varies from region to region. In order to model this variability, we
first applied the parametric texture algorithm of Portilla and Simoncelli to
image patches of 64X64 pixels in a large database of natural images such that
each image patch is then described by 655 texture parameters which specify
certain statistics, such as variances and covariances of wavelet coefficients
or coefficient magnitudes within that patch.
To model the statistics of these texture parameters, we then developed
suitable nonlinear transformations of the parameters that allowed us to fit
their joint statistics with a multivariate Gaussian distribution. We find that
the first 200 principal components contain more than 99% of the variance and
are sufficient to generate textures that are perceptually extremely close to
those generated with all 655 components. We demonstrate the usefulness of the
model in several ways: (1) We sample ensembles of texture patches that can be
directly compared to samples of patches from the natural image database and can
to a high degree reproduce their perceptual appearance. (2) We further
developed an image compression algorithm which generates surprisingly accurate
images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how
our approach can be used for an efficient and objective evaluation of samples
generated with probabilistic models of natural images.Comment: 34 pages, 9 figure
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
We propose a robust approach for performing automatic species-level
recognition of fossil pollen grains in microscopy images that exploits both
global shape and local texture characteristics in a patch-based matching
methodology. We introduce a novel criteria for selecting meaningful and
discriminative exemplar patches. We optimize this function during training
using a greedy submodular function optimization framework that gives a
near-optimal solution with bounded approximation error. We use these selected
exemplars as a dictionary basis and propose a spatially-aware sparse coding
method to match testing images for identification while maintaining global
shape correspondence. To accelerate the coding process for fast matching, we
introduce a relaxed form that uses spatially-aware soft-thresholding during
coding. Finally, we carry out an experimental study that demonstrates the
effectiveness and efficiency of our exemplar selection and classification
mechanisms, achieving accuracy on a difficult fine-grained species
classification task distinguishing three types of fossil spruce pollen.Comment: CVMI 201
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