231 research outputs found
Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case
Following the seminal work of Nesterov, accelerated optimization methods have
been used to powerfully boost the performance of first-order, gradient-based
parameter estimation in scenarios where second-order optimization strategies
are either inapplicable or impractical. Not only does accelerated gradient
descent converge considerably faster than traditional gradient descent, but it
also performs a more robust local search of the parameter space by initially
overshooting and then oscillating back as it settles into a final
configuration, thereby selecting only local minimizers with a basis of
attraction large enough to contain the initial overshoot. This behavior has
made accelerated and stochastic gradient search methods particularly popular
within the machine learning community. In their recent PNAS 2016 paper,
Wibisono, Wilson, and Jordan demonstrate how a broad class of accelerated
schemes can be cast in a variational framework formulated around the Bregman
divergence, leading to continuum limit ODE's. We show how their formulation may
be further extended to infinite dimension manifolds (starting here with the
geometric space of curves and surfaces) by substituting the Bregman divergence
with inner products on the tangent space and explicitly introducing a
distributed mass model which evolves in conjunction with the object of interest
during the optimization process. The co-evolving mass model, which is
introduced purely for the sake of endowing the optimization with helpful
dynamics, also links the resulting class of accelerated PDE based optimization
schemes to fluid dynamical formulations of optimal mass transport
Quickest Moving Object Detection
We present a general framework and method for simultaneous detection and
segmentation of an object in a video that moves (or comes into view of the
camera) at some unknown time in the video. The method is an online approach
based on motion segmentation, and it operates under dynamic backgrounds caused
by a moving camera or moving nuisances. The goal of the method is to detect and
segment the object as soon as it moves. Due to stochastic variability in the
video and unreliability of the motion signal, several frames are needed to
reliably detect the object. The method is designed to detect and segment with
minimum delay subject to a constraint on the false alarm rate. The method is
derived as a problem of Quickest Change Detection. Experiments on a dataset
show the effectiveness of our method in minimizing detection delay subject to
false alarm constraints
SurfCut: Surfaces of Minimal Paths From Topological Structures
We present SurfCut, an algorithm for extracting a smooth, simple surface with
an unknown 3D curve boundary from a noisy 3D image and a seed point. Our method
is built on the novel observation that certain ridge curves of a function
defined on a front propagated using the Fast Marching algorithm lie on the
surface. Our method extracts and cuts these ridges to form the surface
boundary. Our surface extraction algorithm is built on the novel observation
that the surface lies in a valley of the distance from Fast Marching. We show
that the resulting surface is a collection of minimal paths. Using the
framework of cubical complexes and Morse theory, we design algorithms to
extract these critical structures robustly. Experiments on three 3D datasets
show the robustness of our method, and that it achieves higher accuracy with
lower computational cost than state-of-the-art
Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms
We consider the problem of optimization of cost functionals on the
infinite-dimensional manifold of diffeomorphisms. We present a new class of
optimization methods, valid for any optimization problem setup on the space of
diffeomorphisms by generalizing Nesterov accelerated optimization to the
manifold of diffeomorphisms. While our framework is general for infinite
dimensional manifolds, we specifically treat the case of diffeomorphisms,
motivated by optical flow problems in computer vision. This is accomplished by
building on a recent variational approach to a general class of accelerated
optimization methods by Wibisono, Wilson and Jordan, which applies in finite
dimensions. We generalize that approach to infinite dimensional manifolds. We
derive the surprisingly simple continuum evolution equations, which are partial
differential equations, for accelerated gradient descent, and relate it to
simple mechanical principles from fluid mechanics. Our approach has natural
connections to the optimal mass transport problem. This is because one can
think of our approach as an evolution of an infinite number of particles
endowed with mass (represented with a mass density) that moves in an energy
landscape. The mass evolves with the optimization variable, and endows the
particles with dynamics. This is different than the finite dimensional case
where only a single particle moves and hence the dynamics does not depend on
the mass. We derive the theory, compute the PDEs for accelerated optimization,
and illustrate the behavior of these new accelerated optimization schemes
Matching Through Features and Features Through Matching
This paper addresses how to construct features for the problem of image
correspondence, in particular, the paper addresses how to construct features so
as to maintain the right level of invariance versus discriminability. We show
that without additional prior knowledge of the 3D scene, the right tradeoff
cannot be established in a pre-processing step of the images as is typically
done in most feature-based matching methods. However, given knowledge of the
second image to match, the tradeoff between invariance and discriminability of
features in the first image is less ambiguous. This suggests to setup the
problem of feature extraction and matching as a joint estimation problem. We
develop a possible mathematical framework, a possible computational algorithm,
and we give example demonstration on finding correspondence on images related
by a scene that undergoes large 3D deformation of non-planar objects and camera
viewpoint change
Accelerated PDE's for efficient solution of regularized inversion problems
We further develop a new framework, called PDE Acceleration, by applying it
to calculus of variations problems defined for general functions on
, obtaining efficient numerical algorithms to solve the resulting
class of optimization problems based on simple discretizations of their
corresponding accelerated PDE's. While the resulting family of PDE's and
numerical schemes are quite general, we give special attention to their
application for regularized inversion problems, with particular illustrative
examples on some popular image processing applications. The method is a
generalization of momentum, or accelerated, gradient descent to the PDE
setting. For elliptic problems, the descent equations are a nonlinear damped
wave equation, instead of a diffusion equation, and the acceleration is
realized as an improvement in the CFL condition from (for diffusion) to (for wave equations). We work
out several explicit as well as a semi-implicit numerical schemes, together
with their necessary stability constraints, and include recursive update
formulations which allow minimal-effort adaptation of existing gradient descent
PDE codes into the accelerated PDE framework. We explore these schemes more
carefully for a broad class of regularized inversion applications, with special
attention to quadratic, Beltrami, and Total Variation regularization, where the
accelerated PDE takes the form of a nonlinear wave equation. Experimental
examples demonstrate the application of these schemes for image denoising,
deblurring, and inpainting, including comparisons against Primal Dual, Split
Bregman, and ADMM algorithms
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces
We formulate a general energy and method for segmentation that is designed to
have preference for segmenting the coarse structure over the fine structure of
the data, without smoothing across boundaries of regions. The energy is
formulated by considering data terms at a continuum of scales from the scale
space computed from the Heat Equation within regions, and integrating these
terms over all time. We show that the energy may be approximately optimized
without solving for the entire scale space, but rather solving time-independent
linear equations at the native scale of the image, making the method
computationally feasible. We provide a multi-region scheme, and apply our
method to motion segmentation. Experiments on a benchmark dataset shows that
our method is less sensitive to clutter or other undesirable fine-scale
structure, and leads to better performance in motion segmentation
Shape Tracking With Occlusions via Coarse-To-Fine Region-Based Sobolev Descent
We present a method to track the precise shape of an object in video based on
new modeling and optimization on a new Riemannian manifold of parameterized
regions.
Joint dynamic shape and appearance models, in which a template of the object
is propagated to match the object shape and radiance in the next frame, are
advantageous over methods employing global image statistics in cases of complex
object radiance and cluttered background. In cases of 3D object motion and
viewpoint change, self-occlusions and dis-occlusions of the object are
prominent, and current methods employing joint shape and appearance models are
unable to adapt to new shape and appearance information, leading to inaccurate
shape detection. In this work, we model self-occlusions and dis-occlusions in a
joint shape and appearance tracking framework.
Self-occlusions and the warp to propagate the template are coupled, thus a
joint problem is formulated. We derive a coarse-to-fine optimization scheme,
advantageous in object tracking, that initially perturbs the template by coarse
perturbations before transitioning to finer-scale perturbations, traversing all
scales, seamlessly and automatically. The scheme is a gradient descent on a
novel infinite-dimensional Riemannian manifold that we introduce. The manifold
consists of planar parameterized regions, and the metric that we introduce is a
novel Sobolev-type metric defined on infinitesimal vector fields on regions.
The metric has the property of resulting in a gradient descent that
automatically favors coarse-scale deformations (when they reduce the energy)
before moving to finer-scale deformations.
Experiments on video exhibiting occlusion/dis-occlusion, complex radiance and
background show that occlusion/dis-occlusion modeling leads to superior shape
accuracy compared to recent methods employing joint shape/appearance models or
employing global statistics.Comment: Extension of ICCV paper, added coarse-to-fine optimization based on
new Riemannian manifold of parameterized region
Minimum Delay Object Detection From Video
We consider the problem of detecting objects, as they come into view, from
videos in an online fashion. We provide the first real-time solution that is
guaranteed to minimize the delay, i.e., the time between when the object comes
in view and the declared detection time, subject to acceptable levels of
detection accuracy. The method leverages modern CNN-based object detectors that
operate on a single frame, to aggregate detection results over frames to
provide reliable detection at a rate, specified by the user, in guaranteed
minimal delay. To do this, we formulate the problem as a Quickest Detection
problem, which provides the aforementioned guarantees. We derive our algorithms
from this theory. We show in experiments, that with an overhead of just 50 fps,
we can increase the number of correct detections and decrease the overall
computational cost compared to running a modern single-frame detector.Comment: ICCV 201
Optical, Structural and Elemental Analysis of New Nonlinear Organic Single Crystal of Urea L-Asparagine
Single crystal of urea L-asparagine was synthesized and crystallized by slow evaporation solution growth \ud
method at room temperature. The bright, transparent and colourless crystal was obtained with average \ud
dimension of 11 × 0.7 × 0.3 cm3\ud
. The elemental analysis of the compound confirms the stoichiometric ratio of \ud
the compound. The sharp and well defined Bragg peaks obtained in the powder X-ray diffraction pattern \ud
confirm the crystalline nature of the compound. The optical property of the compound was ascertained through \ud
UV-visible spectral analysis. The various characteristics absorption bands in the compound were assigned \ud
through fourier transform infra-red (FTIR) spectroscopy. The single crystal unit cell parameters of the \ud
compound show that the grown crystal belongs to orthorhombic system with space group P. The nonlinear \ud
optical property study indicates that the compound has SHG efficiency 0.5 times greater than that of standard \ud
potassium dihydrogen phosphate (KDP)
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