388,309 research outputs found
ShARc: Shape and Appearance Recognition for Person Identification In-the-wild
Identifying individuals in unconstrained video settings is a valuable yet
challenging task in biometric analysis due to variations in appearances,
environments, degradations, and occlusions. In this paper, we present ShARc, a
multimodal approach for video-based person identification in uncontrolled
environments that emphasizes 3-D body shape, pose, and appearance. We introduce
two encoders: a Pose and Shape Encoder (PSE) and an Aggregated Appearance
Encoder (AAE). PSE encodes the body shape via binarized silhouettes, skeleton
motions, and 3-D body shape, while AAE provides two levels of temporal
appearance feature aggregation: attention-based feature aggregation and
averaging aggregation. For attention-based feature aggregation, we employ
spatial and temporal attention to focus on key areas for person distinction.
For averaging aggregation, we introduce a novel flattening layer after
averaging to extract more distinguishable information and reduce overfitting of
attention. We utilize centroid feature averaging for gallery registration. We
demonstrate significant improvements over existing state-of-the-art methods on
public datasets, including CCVID, MEVID, and BRIAR.Comment: WACV 202
Vortex shape in unbaffled stirred vessels: experimental study via digital image analysis
There is a growing interest in using unbaffled stirred tanks for addressing certain
processing needs. In this work, digital image analysis coupled with a suitable
shadowgraphy-based technique is used to investigate the shape of the free-surface
vortex that forms in uncovered unbaffled stirred tanks.
The technique is based on back-lighting the vessel and suitably averaging vortex shape
over time. Impeller clearance from vessel bottom and tank filling level are varied to
investigate their influence on vortex shape. A correlation is finally proposed to fully
describe vortex shape also when the vortex encompasses the impeller
Bayesian Estimation of White Matter Atlas from High Angular Resolution Diffusion Imaging
We present a Bayesian probabilistic model to estimate the brain white matter
atlas from high angular resolution diffusion imaging (HARDI) data. This model
incorporates a shape prior of the white matter anatomy and the likelihood of
individual observed HARDI datasets. We first assume that the atlas is generated
from a known hyperatlas through a flow of diffeomorphisms and its shape prior
can be constructed based on the framework of large deformation diffeomorphic
metric mapping (LDDMM). LDDMM characterizes a nonlinear diffeomorphic shape
space in a linear space of initial momentum uniquely determining diffeomorphic
geodesic flows from the hyperatlas. Therefore, the shape prior of the HARDI
atlas can be modeled using a centered Gaussian random field (GRF) model of the
initial momentum. In order to construct the likelihood of observed HARDI
datasets, it is necessary to study the diffeomorphic transformation of
individual observations relative to the atlas and the probabilistic
distribution of orientation distribution functions (ODFs). To this end, we
construct the likelihood related to the transformation using the same
construction as discussed for the shape prior of the atlas. The probabilistic
distribution of ODFs is then constructed based on the ODF Riemannian manifold.
We assume that the observed ODFs are generated by an exponential map of random
tangent vectors at the deformed atlas ODF. Hence, the likelihood of the ODFs
can be modeled using a GRF of their tangent vectors in the ODF Riemannian
manifold. We solve for the maximum a posteriori using the
Expectation-Maximization algorithm and derive the corresponding update
equations. Finally, we illustrate the HARDI atlas constructed based on a
Chinese aging cohort of 94 adults and compare it with that generated by
averaging the coefficients of spherical harmonics of the ODF across subjects
Investigation of a new method for improving image resolution for camera tracking applications
Camera based systems have been a preferred choice in many motion tracking applications due to the ease of installation and the ability to work in unprepared environments. The concept of these systems is based on extracting image information (colour and shape properties) to detect the object location. However, the resolution of the image and the camera field-of- view (FOV) are two main factors that can restrict the tracking applications for which these systems can be used. Resolution can be addressed partially by using higher resolution cameras but this may not always be possible or cost effective.
This research paper investigates a new method utilising averaging of offset images to improve the effective resolution using a standard camera. The initial results show that the minimum detectable position change of a tracked object could be improved by up to 4 times
The dependence of test-mass thermal noises on beam shape in gravitational-wave interferometers
In second-generation, ground-based interferometric gravitational-wave
detectors such as Advanced LIGO, the dominant noise at frequencies
Hz to Hz is expected to be due to thermal fluctuations in the
mirrors' substrates and coatings which induce random fluctuations in the shape
of the mirror face. The laser-light beam averages over these fluctuations; the
larger the beam and the flatter its light-power distribution, the better the
averaging and the lower the resulting thermal noise. In semi-infinite mirrors,
scaling laws for the influence of beam shape on the four dominant types of
thermal noise (coating Brownian, coating thermoelastic, substrate Brownian, and
substrate thermoelastic) have been suggested by various researchers and derived
with varying degrees of rigour. Because these scaling laws are important tools
for current research on optimizing the beam shape, it is important to firm up
our understanding of them. This paper (1) gives a summary of the prior work and
of gaps in the prior analyses, (2) gives a unified and rigorous derivation of
all four scaling laws, and (3) explores, relying on work by J. Agresti,
deviations from the scaling laws due to finite mirror size.Comment: 25 pages, 10 figures, submitted to Class. Quantum Gra
OctNetFusion: Learning Depth Fusion from Data
In this paper, we present a learning based approach to depth fusion, i.e.,
dense 3D reconstruction from multiple depth images. The most common approach to
depth fusion is based on averaging truncated signed distance functions, which
was originally proposed by Curless and Levoy in 1996. While this method is
simple and provides great results, it is not able to reconstruct (partially)
occluded surfaces and requires a large number frames to filter out sensor noise
and outliers. Motivated by the availability of large 3D model repositories and
recent advances in deep learning, we present a novel 3D CNN architecture that
learns to predict an implicit surface representation from the input depth maps.
Our learning based method significantly outperforms the traditional volumetric
fusion approach in terms of noise reduction and outlier suppression. By
learning the structure of real world 3D objects and scenes, our approach is
further able to reconstruct occluded regions and to fill in gaps in the
reconstruction. We demonstrate that our learning based approach outperforms
both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric
fusion. Further, we demonstrate state-of-the-art 3D shape completion results.Comment: 3DV 2017, https://github.com/griegler/octnetfusio
Depth perception in disparity-defined objects : finding the balance between averaging and segregation
The work was funded by BBSRC grant BB/J000272/1 and EPSRC grant EP/L505079/1.Deciding what constitutes an object, and what background, is an essential task for the visual system. This presents a conundrum: averaging over the visual scene is required to obtain a precise signal for object segregation, but segregation is required to define the region over which averaging should take place. Depth obtained via binocular disparity (the differences between two eyes’ views), could help with segregation by enabling identification of object and background via differences in depth. Here, we explore depth perception in disparity-defined objects. We show that a simple object segregation rule, followed by averaging over that segregated area, can account for depth estimation errors. To do this, we compared objects with smoothly varying depth edges to those with sharp depth edges, and found that perceived peak depth was reduced for the former. A computational model used a rule based on object shape to segregate and average over a central portion of the object, and was able to emulate the reduction in perceived depth. We also demonstrated that the segregated area is not predefined but is dependent on the object shape. We discuss how this segregation strategy could be employed by animals seeking to deter binocular predators.Publisher PDFPeer reviewe
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