837 research outputs found
Geodesic Transport Barriers in Jupiter's Atmosphere: A Video-Based Analysis
Jupiter's zonal jets and Great Red Spot are well known from still images. Yet
the planet's atmosphere is highly unsteady, which suggests that the actual
material transport barriers delineating its main features should be
time-dependent. Rare video footages of Jupiter's clouds provide an opportunity
to verify this expectation from optically reconstructed velocity fields.
Available videos, however, provide short-time and temporally aperiodic velocity
fields that defy classical dynamical systems analyses focused on asymptotic
features. To this end, we use here the recent theory of geodesic transport
barriers to uncover finite-time mixing barriers in the wind field extracted
from a video captured by NASA's Cassini space mission. More broadly, the
approach described here provides a systematic and frame-invariant way to
extract dynamic coherent structures from time-resolved remote observations of
unsteady continua
Objective Eulerian Coherent Structures
We define objective Eulerian Coherent Structures (OECSs) in two-dimensional,
non-autonomous dynamical systems as instantaneously most influential material
curves. Specifically, OECSs are stationary curves of the averaged instantaneous
material stretching-rate or material shearing-rate functionals. From these
objective (frame-invariant) variational principles, we obtain explicit
differential equations for hyperbolic, elliptic and parabolic OECSs. As
illustration, we compute OECSs in an unsteady ocean velocity data set. In
comparison to structures suggested by other common Eulerian diagnostic tools,
we find OECSs to be the correct short-term cores of observed trajectory
deformation patterns
A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognition
In this paper, we study the problem of facial expression recognition using a
novel space-time geometric representation. We describe the temporal evolution
of facial landmarks as parametrized trajectories on the Riemannian manifold of
positive semidefinite matrices of fixed-rank. Our representation has the
advantage to bring naturally a second desirable quantity when comparing shapes
-- the spatial covariance -- in addition to the conventional affine-shape
representation. We derive then geometric and computational tools for
rate-invariant analysis and adaptive re-sampling of trajectories, grounding on
the Riemannian geometry of the manifold. Specifically, our approach involves
three steps: 1) facial landmarks are first mapped into the Riemannian manifold
of positive semidefinite matrices of rank 2, to build time-parameterized
trajectories; 2) a temporal alignment is performed on the trajectories,
providing a geometry-aware (dis-)similarity measure between them; 3) finally,
pairwise proximity function SVM (ppfSVM) is used to classify them,
incorporating the latter (dis-)similarity measure into the kernel function. We
show the effectiveness of the proposed approach on four publicly available
benchmarks (CK+, MMI, Oulu-CASIA, and AFEW). The results of the proposed
approach are comparable to or better than the state-of-the-art methods when
involving only facial landmarks.Comment: To be appeared at ICCV 201
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics
Existing dimensionality reduction methods are adept at revealing hidden
underlying manifolds arising from high-dimensional data and thereby producing a
low-dimensional representation. However, the smoothness of the manifolds
produced by classic techniques over sparse and noisy data is not guaranteed. In
fact, the embedding generated using such data may distort the geometry of the
manifold and thereby produce an unfaithful embedding. Herein, we propose a
framework for nonlinear dimensionality reduction that generates a manifold in
terms of smooth geodesics that is designed to treat problems in which manifold
measurements are either sparse or corrupted by noise. Our method generates a
network structure for given high-dimensional data using a nearest neighbors
search and then produces piecewise linear shortest paths that are defined as
geodesics. Then, we fit points in each geodesic by a smoothing spline to
emphasize the smoothness. The robustness of this approach for sparse and noisy
datasets is demonstrated by the implementation of the method on synthetic and
real-world datasets.Comment: 13 pages, 7 figures, submitted to Pattern Recognitio
A Critical Comparison of Lagrangian Methods for Coherent Structure Detection
We review and test twelve different approaches to the detection of
finite-time coherent material structures in two-dimensional, temporally
aperiodic flows. We consider both mathematical methods and diagnostic scalar
fields, comparing their performance on three benchmark examples: the
quasiperiodically forced Bickley jet, a two-dimensional turbulence simulation,
and an observational wind velocity field from Jupiter's atmosphere. A close
inspection of the results reveals that the various methods often produce very
different predictions for coherent structures, once they are evaluated beyond
heuristic visual assessment. As we find by passive advection of the coherent
set candidates, false positives and negatives can be produced even by some of
the mathematically justified methods due to the ineffectiveness of their
underlying coherence principles in certain flow configurations. We summarize
the inferred strengths and weaknesses of each method, and make general
recommendations for minimal self-consistency requirements that any Lagrangian
coherence detection technique should satisfy
Visualizing Time-Varying Particle Flows with Diffusion Geometry
The tasks of identifying separation structures and clusters in flow data are
fundamental to flow visualization. Significant work has been devoted to these
tasks in flow represented by vector fields, but there are unique challenges in
addressing these tasks for time-varying particle data. The unstructured nature
of particle data, nonuniform and sparse sampling, and the inability to access
arbitrary particles in space-time make it difficult to define separation and
clustering for particle data. We observe that weaker notions of separation and
clustering through continuous measures of these structures are meaningful when
coupled with user exploration. We achieve this goal by defining a measure of
particle similarity between pairs of particles. More specifically, separation
occurs when spatially-localized particles are dissimilar, while clustering is
characterized by sets of particles that are similar to one another. To be
robust to imperfections in sampling we use diffusion geometry to compute
particle similarity. Diffusion geometry is parameterized by a scale that allows
a user to explore separation and clustering in a continuous manner. We
illustrate the benefits of our technique on a variety of 2D and 3D flow
datasets, from particles integrated in fluid simulations based on time-varying
vector fields, to particle-based simulations in astrophysics.Comment: 14 pages, 16 figures, under revie
A landmark-based algorithm for automatic pattern recognition and abnormality detection
We study a class of mathematical and statistical algorithms with the aim of
establishing a computer-based framework for fast and reliable automatic
abnormality detection on landmark represented image templates. Under this
framework, we apply a landmark-based algorithm for finding a group average as
an estimator that is said to best represent the common features of the group in
study. This algorithm extracts information of momentum at each landmark through
the process of template matching. If ever converges, the proposed algorithm
produces a local coordinate system for each member of the observing group, in
terms of the residual momentum. We use a Bayesian approach on the collected
residual momentum representations for making inference. For illustration, we
apply this framework to a small database of brain images for detecting
structure abnormality. The brain structure changes identified by our framework
are highly consistent with studies in the literature
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection
The uniqueness, complexity, and diversity of facial shapes and expressions
led to success of facial biometric systems. Regardless of the accuracy of
current facial recognition methods, most of them are vulnerable against the
presentation of sophisticated masks. In the highly monitored application
scenario at airports and banks, fraudsters probably do not wear masks. However,
a deception will become more probable due to the increase of unsupervised
authentication using kiosks, eGates and mobile phones in self-service. To
robustly detect elastic 3D masks, one of the ultimate goals is to automatically
analyze the plausibility of the facial behavior based on a sequence of 3D face
scans. Most importantly, such a method would also detect all less advanced
presentation attacks using static 3D masks, bent photographs with eyeholes, and
replay attacks using monitors. Our proposed method achieves this goal by
comparing the temporal curvature change between presentation attacks and
genuine faces. For evaluation purposes, we recorded a challenging database
containing replay attacks, static and elastic 3D masks using a high-quality 3D
sensor. Based on the proposed representation, we found a clear separation
between the low facial expressiveness of presentation attacks and the plausible
behavior of genuine faces.Comment: Manuscript submitted for publication in IEEE International Conference
on Automatic Face & Gesture Recognition (FG
Fitting tree model with CNN and geodesics to track vesselsand application to Ultrasound Localization Microscopy data
Segmentation of tubular structures in vascular imaging is a well studied
task, although it is rare that we try to infuse knowledge of the tree-like
structure of the regions to be detected. Our work focuses on detecting the
important landmarks in the vascular network (via CNN performing both
localization and classification of the points of interest) and representing
vessels as the edges in some minimal distance tree graph. We leverage geodesic
methods relevant to the detection of vessels and their geometry, making use of
the space of positions and orientations so that 2D vessels can be accurately
represented as trees. We build our model to carry tracking on Ultrasound
Localization Microscopy (ULM) data, proposing to build a good cost function for
tracking on this type of data. We also test our framework on synthetic and eye
fundus data. Results show that scarcity of well annotated ULM data is an
obstacle to localization of vascular landmarks but the Orientation Score built
from ULM data yields good geodesics for tracking blood vessels.Comment: This work has been submitted to the IEEE for possible publication.
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Elastic Functional Coding of Riemannian Trajectories
Visual observations of dynamic phenomena, such as human actions, are often
represented as sequences of smoothly-varying features . In cases where the
feature spaces can be structured as Riemannian manifolds, the corresponding
representations become trajectories on manifolds. Analysis of these
trajectories is challenging due to non-linearity of underlying spaces and
high-dimensionality of trajectories. In vision problems, given the nature of
physical systems involved, these phenomena are better characterized on a
low-dimensional manifold compared to the space of Riemannian trajectories. For
instance, if one does not impose physical constraints of the human body, in
data involving human action analysis, the resulting representation space will
have highly redundant features. Learning an effective, low-dimensional
embedding for action representations will have a huge impact in the areas of
search and retrieval, visualization, learning, and recognition. The difficulty
lies in inherent non-linearity of the domain and temporal variability of
actions that can distort any traditional metric between trajectories. To
overcome these issues, we use the framework based on transported square-root
velocity fields (TSRVF); this framework has several desirable properties,
including a rate-invariant metric and vector space representations. We propose
to learn an embedding such that each action trajectory is mapped to a single
point in a low-dimensional Euclidean space, and the trajectories that differ
only in temporal rates map to the same point. We utilize the TSRVF
representation, and accompanying statistical summaries of Riemannian
trajectories, to extend existing coding methods such as PCA, KSVD and Label
Consistent KSVD to Riemannian trajectories or more generally to Riemannian
functions.Comment: Under major revision at IEEE T-PAMI, 201
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