26,759 research outputs found
Dynamic and Scalable Large Scale Image Reconstruction
Recent approaches to reconstructing city-sized areas from large image collections usually process them all at once and only produce disconnected descriptions of image subsets, which typically correspond to major landmarks. In contrast, we propose a framework that lets us take advantage of the available meta-data to build a single, consistent description from these potentially disconnected descriptions. Furthermore, this description can be incrementally updated and enriched as new images become avail- able. We demonstrate the power of our approach by building large-scale reconstructions using images of Lausanne and Prague
Graphical user interface for the project "dynamic" and scalable large scale image reconstruction
En
este
proyecto
se
lleva
a
cabo
la
implementación
de
una
interfaz
gráfica
que
dé
uso
al
proyecto
inicial
de
escalado
de
imágenes.
La
idea
principal
es
que
el
usuario
pueda
manejar
la
información
dada
en
forma
de
nube
de
puntos
para
recrear
edificios.
En
el
proyecto
se
pretende
crear
una
interfaz
gráfica
lo
más
fácil
y
manejable
posible
para
el
usuario,
haciendo
todos
los
cálculos
matemáticos
de
forma
transparente.
Esos
cálculos
consistirán
en
aproximar
de
la
mejor
forma
posible
los
edificios
elegidos
por
el
usuario
en
las
imágenes
al
entorno
tridimensional.
Por
último,
también
se
permitie
guardar
toda
la
información
y
datos
de
los
edificios
recreados.Escuela Técnica Superior de IngenierÃa de TelecomunicaciónUniversidad Politécnica de Cartagen
Streaming an image through the eye: The retina seen as a dithered scalable image coder
We propose the design of an original scalable image coder/decoder that is
inspired from the mammalians retina. Our coder accounts for the time-dependent
and also nondeterministic behavior of the actual retina. The present work
brings two main contributions: As a first step, (i) we design a deterministic
image coder mimicking most of the retinal processing stages and then (ii) we
introduce a retinal noise in the coding process, that we model here as a dither
signal, to gain interesting perceptual features. Regarding our first
contribution, our main source of inspiration will be the biologically plausible
model of the retina called Virtual Retina. The main novelty of this coder is to
show that the time-dependent behavior of the retina cells could ensure, in an
implicit way, scalability and bit allocation. Regarding our second
contribution, we reconsider the inner layers of the retina. We emit a possible
interpretation for the non-determinism observed by neurophysiologists in their
output. For this sake, we model the retinal noise that occurs in these layers
by a dither signal. The dithering process that we propose adds several
interesting features to our image coder. The dither noise whitens the
reconstruction error and decorrelates it from the input stimuli. Furthermore,
integrating the dither noise in our coder allows a faster recognition of the
fine details of the image during the decoding process. Our present paper goal
is twofold. First, we aim at mimicking as closely as possible the retina for
the design of a novel image coder while keeping encouraging performances.
Second, we bring a new insight concerning the non-deterministic behavior of the
retina.Comment: arXiv admin note: substantial text overlap with arXiv:1104.155
A randomised primal-dual algorithm for distributed radio-interferometric imaging
Next generation radio telescopes, like the Square Kilometre Array, will
acquire an unprecedented amount of data for radio astronomy. The development of
fast, parallelisable or distributed algorithms for handling such large-scale
data sets is of prime importance. Motivated by this, we investigate herein a
convex optimisation algorithmic structure, based on primal-dual
forward-backward iterations, for solving the radio interferometric imaging
problem. It can encompass any convex prior of interest. It allows for the
distributed processing of the measured data and introduces further flexibility
by employing a probabilistic approach for the selection of the data blocks used
at a given iteration. We study the reconstruction performance with respect to
the data distribution and we propose the use of nonuniform probabilities for
the randomised updates. Our simulations show the feasibility of the
randomisation given a limited computing infrastructure as well as important
computational advantages when compared to state-of-the-art algorithmic
structures.Comment: 5 pages, 3 figures, Proceedings of the European Signal Processing
Conference (EUSIPCO) 2016, Related journal publication available at
https://arxiv.org/abs/1601.0402
Scalable Dense Monocular Surface Reconstruction
This paper reports on a novel template-free monocular non-rigid surface
reconstruction approach. Existing techniques using motion and deformation cues
rely on multiple prior assumptions, are often computationally expensive and do
not perform equally well across the variety of data sets. In contrast, the
proposed Scalable Monocular Surface Reconstruction (SMSR) combines strengths of
several algorithms, i.e., it is scalable with the number of points, can handle
sparse and dense settings as well as different types of motions and
deformations. We estimate camera pose by singular value thresholding and
proximal gradient. Our formulation adopts alternating direction method of
multipliers which converges in linear time for large point track matrices. In
the proposed SMSR, trajectory space constraints are integrated by smoothing of
the measurement matrix. In the extensive experiments, SMSR is demonstrated to
consistently achieve state-of-the-art accuracy on a wide variety of data sets.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
A bio-inspired image coder with temporal scalability
We present a novel bio-inspired and dynamic coding scheme for static images.
Our coder aims at reproducing the main steps of the visual stimulus processing
in the mammalian retina taking into account its time behavior. The main novelty
of this work is to show how to exploit the time behavior of the retina cells to
ensure, in a simple way, scalability and bit allocation. To do so, our main
source of inspiration will be the biologically plausible retina model called
Virtual Retina. Following a similar structure, our model has two stages. The
first stage is an image transform which is performed by the outer layers in the
retina. Here it is modelled by filtering the image with a bank of difference of
Gaussians with time-delays. The second stage is a time-dependent
analog-to-digital conversion which is performed by the inner layers in the
retina. Thanks to its conception, our coder enables scalability and bit
allocation across time. Also, our decoded images do not show annoying artefacts
such as ringing and block effects. As a whole, this article shows how to
capture the main properties of a biological system, here the retina, in order
to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS
2011
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