15,834 research outputs found
Self-similar prior and wavelet bases for hidden incompressible turbulent motion
This work is concerned with the ill-posed inverse problem of estimating
turbulent flows from the observation of an image sequence. From a Bayesian
perspective, a divergence-free isotropic fractional Brownian motion (fBm) is
chosen as a prior model for instantaneous turbulent velocity fields. This
self-similar prior characterizes accurately second-order statistics of velocity
fields in incompressible isotropic turbulence. Nevertheless, the associated
maximum a posteriori involves a fractional Laplacian operator which is delicate
to implement in practice. To deal with this issue, we propose to decompose the
divergent-free fBm on well-chosen wavelet bases. As a first alternative, we
propose to design wavelets as whitening filters. We show that these filters are
fractional Laplacian wavelets composed with the Leray projector. As a second
alternative, we use a divergence-free wavelet basis, which takes implicitly
into account the incompressibility constraint arising from physics. Although
the latter decomposition involves correlated wavelet coefficients, we are able
to handle this dependence in practice. Based on these two wavelet
decompositions, we finally provide effective and efficient algorithms to
approach the maximum a posteriori. An intensive numerical evaluation proves the
relevance of the proposed wavelet-based self-similar priors.Comment: SIAM Journal on Imaging Sciences, 201
Design and Autonomous Stabilization of a Ballistically Launched Multirotor
Aircraft that can launch ballistically and convert to autonomous, free flying
drones have applications in many areas such as emergency response, defense, and
space exploration, where they can gather critical situational data using
onboard sensors. This paper presents a ballistically launched, autonomously
stabilizing multirotor prototype (SQUID, Streamlined Quick Unfolding
Investigation Drone) with an onboard sensor suite, autonomy pipeline, and
passive aerodynamic stability. We demonstrate autonomous transition from
passive to vision based, active stabilization, confirming the ability of the
multirotor to autonomously stabilize after a ballistic launch in a GPS denied
environment.Comment: Accepted to 2020 International Conference on Robotics and Automatio
Learning to Interpret Fluid Type Phenomena via Images
Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background pattern. Regarding the recovery of severely downgraded underwater images due to the refractive distortions caused by water surface fluctuations, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. Furthermore, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. We also develop a combinational deep neural network that can simultaneously perform recovery of the latent distortion-free image as well as 3D reconstruction of the transparent and dynamic fluid surface. Through extensive experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural networks outperform the current state-of-the-art on solving specific tasks
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
Enhancement of Underwater Video Mosaics for Post-Processing
Mosaics of seafloor created from still images or video acquired underwater have proved to be useful for construction of maps of forensic and archeological sites, species\u27 abundance estimates, habitat characterization, etc. Images taken by a camera mounted on a stable platform are registered (at first pair-wise and then globally) and assembled in a high resolution visual map of the surveyed area. While this map is usually sufficient for a human orientation and even quantitative measurements, it often contains artifacts that complicate an automatic post-processing (for example, extraction of shapes for organism counting, or segmentation for habitat characterization). The most prominent artifacts are inter-frame seams caused by inhomogeneous artificial illumination, and local feature misalignments due to parallax effects - result of an attempt to represent a 3D world on a 2D map. In this paper we propose two image processing techniques for mosaic quality enhancement - median mosaic-based illumination correction suppressing appearance of inter-frame seams, and micro warping decreasing influence of parallax effects
Decorrelation Times of Photospheric Fields and Flows
We use autocorrelation to investigate evolution in flow fields inferred by
applying Fourier Local Correlation Tracking (FLCT) to a sequence of
high-resolution (0.3 \arcsec), high-cadence ( min) line-of-sight
magnetograms of NOAA active region (AR) 10930 recorded by the Narrowband Filter
Imager (NFI) of the Solar Optical Telescope (SOT) aboard the {\em Hinode}
satellite over 12--13 December 2006. To baseline the timescales of flow
evolution, we also autocorrelated the magnetograms, at several spatial
binnings, to characterize the lifetimes of active region magnetic structures
versus spatial scale. Autocorrelation of flow maps can be used to optimize
tracking parameters, to understand tracking algorithms' susceptibility to
noise, and to estimate flow lifetimes. Tracking parameters varied include: time
interval between magnetogram pairs tracked, spatial binning applied
to the magnetograms, and windowing parameter used in FLCT. Flow
structures vary over a range of spatial and temporal scales (including
unresolved scales), so tracked flows represent a local average of the flow over
a particular range of space and time. We define flow lifetime to be the flow
decorrelation time, . For , tracking results represent
the average velocity over one or more flow lifetimes. We analyze lifetimes of
flow components, divergences, and curls as functions of magnetic field strength
and spatial scale. We find a significant trend of increasing lifetimes of flow
components, divergences, and curls with field strength, consistent with Lorentz
forces partially governing flows in the active photosphere, as well as strong
trends of increasing flow lifetime and decreasing magnitudes with increases in
both spatial scale and .Comment: 48 pages, 20 figures, submitted to the Astrophysical Journal;
full-resolution images in manuscript (8MB) at
http://solarmuri.ssl.berkeley.edu/~welsch/public/manuscripts/flow_lifetimes_v2.pd
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