2,475 research outputs found
Depth filtering for auto-stereoscopic mobile devices
In this work we address a scenario where 3D content is transmitted to a mobile terminal with 3D display capabilities. We consider the use of 2D plus depth format to represent the 3D content and focus on the generation of synthetic views in the terminal. We evaluate different types of smoothing filters that are applied to depth maps with the aim of reducing the disoccluded regions. The evaluation takes into account the reduction of holes in the synthetic view as well as the presence of geometrical distortion caused by the smoothing operation. The selected filter has been included within an implemented module for the VideoLan Client (VLC) software in order to render 3D content from the 2D plus depth data format
Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing
We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionaly, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Semantic Cross-View Matching
Matching cross-view images is challenging because the appearance and
viewpoints are significantly different. While low-level features based on
gradient orientations or filter responses can drastically vary with such
changes in viewpoint, semantic information of images however shows an invariant
characteristic in this respect. Consequently, semantically labeled regions can
be used for performing cross-view matching. In this paper, we therefore explore
this idea and propose an automatic method for detecting and representing the
semantic information of an RGB image with the goal of performing cross-view
matching with a (non-RGB) geographic information system (GIS). A segmented
image forms the input to our system with segments assigned to semantic concepts
such as traffic signs, lakes, roads, foliage, etc. We design a descriptor to
robustly capture both, the presence of semantic concepts and the spatial layout
of those segments. Pairwise distances between the descriptors extracted from
the GIS map and the query image are then used to generate a shortlist of the
most promising locations with similar semantic concepts in a consistent spatial
layout. An experimental evaluation with challenging query images and a large
urban area shows promising results
Space-variant picture coding
PhDSpace-variant picture coding techniques exploit the strong spatial non-uniformity of
the human visual system in order to increase coding efficiency in terms of perceived quality
per bit. This thesis extends space-variant coding research in two directions. The first of
these directions is in foveated coding. Past foveated coding research has been dominated
by the single-viewer, gaze-contingent scenario. However, for research into the multi-viewer
and probability-based scenarios, this thesis presents a missing piece: an algorithm for computing
an additive multi-viewer sensitivity function based on an established eye resolution
model, and, from this, a blur map that is optimal in the sense of discarding frequencies in
least-noticeable- rst order. Furthermore, for the application of a blur map, a novel algorithm
is presented for the efficient computation of high-accuracy smoothly space-variant
Gaussian blurring, using a specialised filter bank which approximates perfect space-variant
Gaussian blurring to arbitrarily high accuracy and at greatly reduced cost compared to
the brute force approach of employing a separate low-pass filter at each image location.
The second direction is that of artifi cially increasing the depth-of- field of an image, an
idea borrowed from photography with the advantage of allowing an image to be reduced
in bitrate while retaining or increasing overall aesthetic quality. Two synthetic depth of field algorithms are presented herein, with the desirable properties of aiming to mimic
occlusion eff ects as occur in natural blurring, and of handling any number of blurring
and occlusion levels with the same level of computational complexity. The merits of this
coding approach have been investigated by subjective experiments to compare it with
single-viewer foveated image coding. The results found the depth-based preblurring to
generally be significantly preferable to the same level of foveation blurring
Coded aperture imaging
This thesis studies the coded aperture camera, a device consisting of a conventional
camera with a modified aperture mask, that enables the recovery
of both depth map and all-in-focus image from a single 2D input image.
Key contributions of this work are the modeling of the statistics of natural
images and the design of efficient blur identification methods in a Bayesian
framework. Two cases are distinguished: 1) when the aperture can be decomposed
in a small set of identical holes, and 2) when the aperture has a
more general configuration. In the first case, the formulation of the problem
incorporates priors about the statistical variation of the texture to avoid
ambiguities in the solution. This allows to bypass the recovery of the sharp
image and concentrate only on estimating depth. In the second case, the
depth reconstruction is addressed via convolutions with a bank of linear
filters. Key advantages over competing methods are the higher numerical
stability and the ability to deal with large blur. The all-in-focus image can
then be recovered by using a deconvolution step with the estimated depth
map. Furthermore, for the purpose of depth estimation alone, the proposed
algorithm does not require information about the mask in use. The
comparison with existing algorithms in the literature shows that the proposed
methods achieve state-of-the-art performance. This solution is also
extended for the first time to images affected by both defocus and motion
blur and, finally, to video sequences with moving and deformable objects
GRO J1744-28: an intermediate B-field pulsar in a low mass X-ray binary
The bursting pulsar, GRO J1744-28, went again in outburst after 18
years of quiescence in mid-January 2014. We studied the broad-band, persistent,
X-ray spectrum using X-ray data from a XMM-Newton observation, performed almost
at the peak of the outburst, and from a close INTEGRAL observation, performed 3
days later, thus covering the 1.3-70.0 keV band. The spectrum shows a complex
continuum shape that cannot be modelled with standard high-mass X-ray pulsar
models, nor by two-components models. We observe broadband and peaked residuals
from 4 to 15 keV, and we propose a self-consistent interpretation of these
residuals, assuming they are produced by cyclotron absorption features and by a
moderately smeared, highly ionized, reflection component. We identify the
cyclotron fundamental at 4.7 keV, with hints for two possible harmonics
at 10.4 keV and 15.8 keV. The position of the cyclotron fundamental allows an
estimate for the pulsar magnetic field of (5.27 0.06) 10
G, if the feature is produced at its surface. From the dynamical and
relativistic smearing of the disk reflected component, we obtain a lower limit
estimate for the truncated accretion disk inner radius, ( 100 R),
and for the inclination angle (18-48). We also detect the
presence of a softer thermal component, that we associate with the emission
from an accretion disk truncated at a distance from the pulsar of 50-115 R.
From these estimates, we derive the magneto-spheric radius for disk accretion
to be 0.2 times the classical Alfv\'en radius for radial accretion.Comment: Accepted for publication in MNRA
Automatic region-of-interest extraction in low depth-of-field images
PhD ThesisAutomatic extraction of focused regions from images with low depth-of-field
(DOF) is a problem without an efficient solution yet. The capability of
extracting focused regions can help to bridge the semantic gap by integrating
image regions which are meaningfully relevant and generally do not exhibit
uniform visual characteristics. There exist two main difficulties for extracting
focused regions from low DOF images using high-frequency based techniques:
computational complexity and performance.
A novel unsupervised segmentation approach based on ensemble clustering is
proposed to extract the focused regions from low DOF images in two stages.
The first stage is to cluster image blocks in a joint contrast-energy feature space
into three constituent groups. To achieve this, we make use of a normal
mixture-based model along with standard expectation-maximization (EM)
algorithm at two consecutive levels of block size. To avoid the common
problem of local optima experienced in many models, an ensemble EM
clustering algorithm is proposed. As a result, relevant blocks, i.e., block-based
region-of-interest (ROI), closely conforming to image objects are extracted.
In stage two, two different approaches have been developed to extract
pixel-based ROI. In the first approach, a binary saliency map is constructed
from the relevant blocks at the pixel level, which is based on difference of
Gaussian (DOG) and binarization methods. Then, a set of morphological
operations is employed to create the pixel-based ROI from the map.
Experimental results demonstrate that the proposed approach achieves an
average segmentation performance of 91.3% and is computationally 3 times
faster than the best existing approach. In the second approach, a minimal graph
cut is constructed by using the max-flow method and also by using
object/background seeds provided by the ensemble clustering algorithm.
Experimental results demonstrate an average segmentation performance of 91.7%
and approximately 50% reduction of the average computational time by the
proposed colour based approach compared with existing unsupervised
approaches
- …