658 research outputs found
Galaxies Going Bananas: Inferring the 3D Geometry of High-Redshift Galaxies with JWST-CEERS
The 3D geometry of high-redshift galaxies remains poorly understood. We build
a differentiable Bayesian model and use Hamiltonian Monte Carlo to efficiently
and robustly infer the 3D shapes of star-forming galaxies in JWST-CEERS
observations with at . We reproduce
previous results from HST-CANDELS in a fraction of the computing time and
constrain the mean ellipticity, triaxiality, size and covariances with samples
as small as galaxies. We find high 3D ellipticities for all
mass-redshift bins suggesting oblate (disky) or prolate (elongated) geometries.
We break that degeneracy by constraining the mean triaxiality to be for
dwarfs at (favoring the prolate scenario),
with significantly lower triaxialities for higher masses and lower redshifts
indicating the emergence of disks. The prolate population traces out a
``banana'' in the projected diagram with an excess of low ,
large galaxies. The dwarf prolate fraction rises from at
to at . If these are disks, they cannot be
axisymmetric but instead must be unusually oval (triaxial) unlike local
circular disks. We simultaneously constrain the 3D size-mass relation and its
dependence on 3D geometry. High-probability prolate and oblate candidates show
remarkably similar S\'ersic indices (), non-parametric morphological
properties and specific star formation rates. Both tend to be visually
classified as disks or irregular but edge-on oblate candidates show more dust
attenuation. We discuss selection effects, follow-up prospects and theoretical
implications.Comment: Submitted to ApJ, main body is 35 pages of which ~half are full-page
figures, comments welcom
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
The Three-Dimensional Circumstellar Environment of SN 1987A
We present the detailed construction and analysis of the most complete map to
date of the circumstellar environment around SN 1987A, using ground and
space-based imaging from the past 16 years. PSF-matched difference-imaging
analyses of data from 1988 through 1997 reveal material between 1 and 28 ly
from the SN. Careful analyses allows the reconstruction of the probable
circumstellar environment, revealing a richly-structured bipolar nebula. An
outer, double-lobed ``Peanut,'' which is believed to be the contact
discontinuity between red supergiant and main sequence winds, is a prolate
shell extending 28 ly along the poles and 11 ly near the equator. Napoleon's
Hat, previously believed to be an independent structure, is the waist of this
Peanut, which is pinched to a radius of 6 ly. Interior to this is a cylindrical
hourglass, 1 ly in radius and 4 ly long, which connects to the Peanut by a
thick equatorial disk. The nebulae are inclined 41\degr south and 8\degr east
of the line of sight, slightly elliptical in cross section, and marginally
offset west of the SN. From the hourglass to the large, bipolar lobes, echo
fluxes suggest that the gas density drops from 1--3 cm^{-3} to >0.03 cm^{-3},
while the maximum dust-grain size increases from ~0.2 micron to 2 micron, and
the Si:C dust ratio decreases. The nebulae have a total mass of ~1.7 Msun. The
geometry of the three rings is studied, suggesting the northern and southern
rings are located 1.3 and 1.0 ly from the SN, while the equatorial ring is
elliptical (b/a < 0.98), and spatially offset in the same direction as the
hourglass.Comment: Accepted for publication in the ApJ Supplements. 38 pages in
apjemulate format, with 52 figure
Content based image pose manipulation
This thesis proposes the application of space-frequency transformations to the domain of pose estimation in images. This idea is explored using the Wavelet Transform with illustrative applications in pose estimation for face images, and images of planar scenes. The approach is based
on examining the spatial frequency components in an image, to allow the inherent scene symmetry balance to be recovered. For face images with restricted pose variation (looking left or right), an algorithm is proposed to maximise this symmetry in order to transform the image
into a fronto-parallel pose. This scheme is further employed to identify the optimal frontal facial pose from a video sequence to automate facial capture processes. These features are an important pre-requisite in facial recognition and expression classification systems. The under
lying principles of this spatial-frequency approach are examined with respect to images with planar scenes. Using the Continuous Wavelet Transform, full perspective planar transformations are estimated within a featureless framework. Restoring central symmetry to the wavelet
transformed images in an iterative optimisation scheme removes this perspective pose. This advances upon existing spatial approaches that require segmentation and feature matching, and frequency only techniques that are limited to affine transformation recovery. To evaluate the proposed techniques, the pose of a database of subjects portraying varying yaw orientations is estimated and the accuracy is measured against the captured ground truth information. Additionally, full perspective homographies for synthesised and imaged textured planes are estimated. Experimental results are presented for both situations that compare favourably with existing techniques in the literature
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3D Shape Understanding and Generation
In recent years, Machine Learning techniques have revolutionized solutions to longstanding image-based problems, like image classification, generation, semantic segmentation, object detection and many others. However, if we want to be able to build agents that can successfully interact with the real world, those techniques need to be capable of reasoning about the world as it truly is: a tridimensional space. There are two main challenges while handling 3D information in machine learning models. First, it is not clear what is the best 3D representation. For images, convolutional neural networks (CNNs) operating on raster images yield the best results in virtually all image-based benchmarks. For 3D data, the best combination of model and representation is still an open question. Second, 3D data is not available on the same scale as images – taking pictures is a common procedure in our daily lives, whereas capturing 3D content is an activity usually restricted to specialized professionals. This thesis is focused on addressing both of these issues. Which model and representation should we use for generating and recognizing 3D data? What are efficient ways of learning 3D representations from a few examples? Is it possible to leverage image data to build models capable of reasoning about the world in 3D?
Our research findings show that it is possible to build models that efficiently generate 3D shapes as irregularly structured representations. Those models require significantly less memory while generating higher quality shapes than the ones based on voxels and multi-view representations. We start by developing techniques to generate shapes represented as point clouds. This class of models leads to high quality reconstructions and better unsupervised feature learning. However, since point clouds are not amenable to editing and human manipulation, we also present models capable of generating shapes as sets of shape handles -- simpler primitives that summarize complex 3D shapes and were specifically designed for high-level tasks and user interaction. Despite their effectiveness, those approaches require some form of 3D supervision, which is scarce. We present multiple alternatives to this problem. First, we investigate how approximate convex decomposition techniques can be used as self-supervision to improve recognition models when only a limited number of labels are available. Second, we study how neural network architectures induce shape priors that can be used in multiple reconstruction tasks -- using both volumetric and manifold representations. In this regime, reconstruction is performed from a single example -- either a sparse point cloud or multiple silhouettes. Finally, we demonstrate how to train generative models of 3D shapes without using any 3D supervision by combining differentiable rendering techniques and Generative Adversarial Networks
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
3D face modelling from sparse data
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Pose-invariant, model-based object recognition, using linear combination of views and Bayesian statistics
This thesis presents an in-depth study on the problem of object recognition, and in particular the detection
of 3-D objects in 2-D intensity images which may be viewed from a variety of angles. A solution to this
problem remains elusive to this day, since it involves dealing with variations in geometry, photometry
and viewing angle, noise, occlusions and incomplete data. This work restricts its scope to a particular
kind of extrinsic variation; variation of the image due to changes in the viewpoint from which the object
is seen.
A technique is proposed and developed to address this problem, which falls into the category of
view-based approaches, that is, a method in which an object is represented as a collection of a small
number of 2-D views, as opposed to a generation of a full 3-D model. This technique is based on the
theoretical observation that the geometry of the set of possible images of an object undergoing 3-D rigid
transformations and scaling may, under most imaging conditions, be represented by a linear combination
of a small number of 2-D views of that object. It is therefore possible to synthesise a novel image of an
object given at least two existing and dissimilar views of the object, and a set of linear coefficients that
determine how these views are to be combined in order to synthesise the new image.
The method works in conjunction with a powerful optimization algorithm, to search and recover the
optimal linear combination coefficients that will synthesize a novel image, which is as similar as possible
to the target, scene view. If the similarity between the synthesized and the target images is above some
threshold, then an object is determined to be present in the scene and its location and pose are defined,
in part, by the coefficients. The key benefits of using this technique is that because it works directly
with pixel values, it avoids the need for problematic, low-level feature extraction and solution of the
correspondence problem. As a result, a linear combination of views (LCV) model is easy to construct
and use, since it only requires a small number of stored, 2-D views of the object in question, and the
selection of a few landmark points on the object, the process which is easily carried out during the offline,
model building stage. In addition, this method is general enough to be applied across a variety of
recognition problems and different types of objects.
The development and application of this method is initially explored looking at two-dimensional
problems, and then extending the same principles to 3-D. Additionally, the method is evaluated across
synthetic and real-image datasets, containing variations in the objects’ identity and pose. Future work on
possible extensions to incorporate a foreground/background model and lighting variations of the pixels
are examined
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