8,607 research outputs found
Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters
Finding suitable features has been an essential problem in computer vision.
We focus on Restricted Boltzmann Machines (RBMs), which, despite their
versatility, cannot accommodate transformations that may occur in the scene. As
a result, several approaches have been proposed that consider a set of
transformations, which are used to either augment the training set or transform
the actual learned filters. In this paper, we propose the Explicit
Rotation-Invariant Restricted Boltzmann Machine, which exploits prior
information coming from the dominant orientation of images. Our model extends
the standard RBM, by adding a suitable number of weight matrices, associated
with each dominant gradient. We show that our approach is able to learn
rotation-invariant features, comparing it with the classic formulation of RBM
on the MNIST benchmark dataset. Overall, requiring less hidden units, our
method learns compact features, which are robust to rotations.Comment: 8 pages, 3 figures, 1 tabl
Towards Dead Time Inclusion in Neuronal Modeling
A mathematical description of the refractoriness period in neuronal diffusion
modeling is given and its moments are explicitly obtained in a form that is
suitable for quantitative evaluations. Then, for the Wiener, Ornstein-Uhlenbeck
and Feller neuronal models, an analysis of the features exhibited by the mean
and variance of the first passage time and of refractoriness period is
performed.Comment: 12 pages, 1 figur
Pansharpening techniques to detect mass monument damaging in Iraq
The recent mass destructions of monuments in Iraq cannot be monitored with the terrestrial survey methodologies, for obvious reasons
of safety. For the same reasons, it’s not advisable the use of classical aerial photogrammetry, so it was obvious to think to the use of
multispectral Very High Resolution (VHR) satellite imagery. Nowadays VHR satellite images resolutions are very near airborne
photogrammetrical images and usually they are acquired in multispectral mode. The combination of the various bands of the images
is called pan-sharpening and it can be carried on using different algorithms and strategies. The correct pansharpening methodology,
for a specific image, must be chosen considering the specific multispectral characteristics of the satellite used and the particular
application. In this paper a first definition of guidelines for the use of VHR multispectral imagery to detect monument destruction in
unsafe area, is reported.
The proposed methodology, agreed with UNESCO and soon to be used in Libya for the coastal area, has produced a first report
delivered to the Iraqi authorities. Some of the most evident examples are reported to show the possible capabilities of identification of
damages using VHR images
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
In recent years, there has been an increasing interest in image-based plant
phenotyping, applying state-of-the-art machine learning approaches to tackle
challenging problems, such as leaf segmentation (a multi-instance problem) and
counting. Most of these algorithms need labelled data to learn a model for the
task at hand. Despite the recent release of a few plant phenotyping datasets,
large annotated plant image datasets for the purpose of training deep learning
algorithms are lacking. One common approach to alleviate the lack of training
data is dataset augmentation. Herein, we propose an alternative solution to
dataset augmentation for plant phenotyping, creating artificial images of
plants using generative neural networks. We propose the Arabidopsis Rosette
Image Generator (through) Adversarial Network: a deep convolutional network
that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a
recent adversarial network model using convolutional layers). Specifically, we
trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset,
containing Arabidopsis Thaliana plants. We show that our model is able to
generate realistic 128x128 colour images of plants. We train our network
conditioning on leaf count, such that it is possible to generate plants with a
given number of leaves suitable, among others, for training regression based
models. We propose a new Ax dataset of artificial plants images, obtained by
our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting
algorithm, showing that the testing error is reduced when Ax is used as part of
the training data.Comment: 8 pages, 6 figures, 1 table, ICCV CVPPP Workshop 201
Ultrasensitive interferometric on-chip microscopy of transparent objects
Light microscopes can detect objects through several physical processes, such as scattering, absorption, and reflection. In transparent objects, these mechanisms are often too weak, and interference effects are more suitable to observe the tiny refractive index variations that produce phase shifts. We propose an on-chip microscope design that exploits birefringence in an unconventional geometry. It makes use of two sheared and quasi-overlapped illuminating beams experiencing relative phase shifts when going through the object, and a complementary metal-oxide-semiconductor image sensor array to record the resulting interference pattern. Unlike conventional microscopes, the beams are unfocused, leading to a very large field of view (20 mm(2)) and detection volume (more than 0.5 cm(3)), at the expense of lateral resolution. The high axial sensitivity (<1 nm) achieved using a novel phase-shifting interferometric operation makes the proposed device ideal for examining transparent substrates and reading microarrays of biomarkers. This is demonstrated by detecting nanometer-thick surface modulations on glass and single and double protein layers.Peer ReviewedPostprint (published version
Ising transition in the two-dimensional quantum Heisenberg model
We study the thermodynamics of the spin- two-dimensional quantum
Heisenberg antiferromagnet on the square lattice with nearest () and
next-nearest () neighbor couplings in its collinear phase (),
using the pure-quantum self-consistent harmonic approximation. Our results show
the persistence of a finite-temperature Ising phase transition for every value
of the spin, provided that the ratio is greater than a critical value
corresponding to the onset of collinear long-range order at zero temperature.
We also calculate the spin- and temperature-dependence of the collinear
susceptibility and correlation length, and we discuss our results in light of
the experiments on LiVOSiO and related compounds.Comment: 4 page, 4 figure
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