57,805 research outputs found
Point-wise mutual information-based video segmentation with high temporal consistency
In this paper, we tackle the problem of temporally consistent boundary
detection and hierarchical segmentation in videos. While finding the best
high-level reasoning of region assignments in videos is the focus of much
recent research, temporal consistency in boundary detection has so far only
rarely been tackled. We argue that temporally consistent boundaries are a key
component to temporally consistent region assignment. The proposed method is
based on the point-wise mutual information (PMI) of spatio-temporal voxels.
Temporal consistency is established by an evaluation of PMI-based point
affinities in the spectral domain over space and time. Thus, the proposed
method is independent of any optical flow computation or previously learned
motion models. The proposed low-level video segmentation method outperforms the
learning-based state of the art in terms of standard region metrics
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201
Level Set Methods for Stochastic Discontinuity Detection in Nonlinear Problems
Stochastic physical problems governed by nonlinear conservation laws are
challenging due to solution discontinuities in stochastic and physical space.
In this paper, we present a level set method to track discontinuities in
stochastic space by solving a Hamilton-Jacobi equation. By introducing a speed
function that vanishes at discontinuities, the iso-zero of the level set
problem coincide with the discontinuities of the conservation law. The level
set problem is solved on a sequence of successively finer grids in stochastic
space. The method is adaptive in the sense that costly evaluations of the
conservation law of interest are only performed in the vicinity of the
discontinuities during the refinement stage. In regions of stochastic space
where the solution is smooth, a surrogate method replaces expensive evaluations
of the conservation law. The proposed method is tested in conjunction with
different sets of localized orthogonal basis functions on simplex elements, as
well as frames based on piecewise polynomials conforming to the level set
function. The performance of the proposed method is compared to existing
adaptive multi-element generalized polynomial chaos methods
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Rethinking Secure Precoding via Interference Exploitation: A Smart Eavesdropper Perspective
Based on the concept of constructive interference (CI), multiuser
interference (MUI) has recently been shown to be beneficial for communication
secrecy. A few CI-based secure precoding algorithms have been proposed that use
both the channel state information (CSI) and knowledge of the instantaneous
transmit symbols. In this paper, we examine the CI-based secure precoding
problem with a focus on smart eavesdroppers that exploit statistical
information gleaned from the precoded data for symbol detection. Moreover, the
impact of correlation between the main and eavesdropper channels is taken into
account. We first modify an existing CI-based preocding scheme to better
utilize the destructive impact of the interference. Then, we point out the
drawback of both the existing and the new modified CI-based precoders when
faced with a smart eavesdropper. To address this deficiency, we provide a
general principle for precoder design and then give two specific design
examples. Finally, the scenario where the eavesdropper's CSI is unavailable is
studied. Numerical results show that although our modified CI-based precoder
can achieve a better energy-secrecy trade-off than the existing approach, both
have a limited secrecy benefit. On the contrary, the precoders developed using
the new CI-design principle can achieve a much improved trade-off and
significantly degrade the eavesdropper's performance
Provable Self-Representation Based Outlier Detection in a Union of Subspaces
Many computer vision tasks involve processing large amounts of data
contaminated by outliers, which need to be detected and rejected. While outlier
detection methods based on robust statistics have existed for decades, only
recently have methods based on sparse and low-rank representation been
developed along with guarantees of correct outlier detection when the inliers
lie in one or more low-dimensional subspaces. This paper proposes a new outlier
detection method that combines tools from sparse representation with random
walks on a graph. By exploiting the property that data points can be expressed
as sparse linear combinations of each other, we obtain an asymmetric affinity
matrix among data points, which we use to construct a weighted directed graph.
By defining a suitable Markov Chain from this graph, we establish a connection
between inliers/outliers and essential/inessential states of the Markov chain,
which allows us to detect outliers by using random walks. We provide a
theoretical analysis that justifies the correctness of our method under
geometric and connectivity assumptions. Experimental results on image databases
demonstrate its superiority with respect to state-of-the-art sparse and
low-rank outlier detection methods.Comment: 16 pages. CVPR 2017 spotlight oral presentatio
Microfluidic multipoles: theory and applications
Microfluidic multipoles (MFMs) have been realized experimentally and hold
promise for "open-space" biological and chemical surface processing. Whereas
convective flow can readily be predicted using hydraulic-electrical analogies,
the design of advanced MFMs is constrained by the lack of simple, accurate
models to predict mass transport within them. In this work, we introduce the
first exact solutions to mass transport in multipolar microfluidics based on
the iterative conformal mapping of 2D advection-diffusion around a simple edge
into dipoles and multipolar geometries, revealing a rich landscape of transport
modes. The models were validated experimentally with a library of 3D printed
MFM devices and found in excellent agreement. Following a theory-guided design
approach, we further ideated and fabricated two new classes of spatiotemporally
reconfigurable MFM devices that are used for processing surfaces with
time-varying reagent streams, and to realize a multistep automated immunoassay.
Overall, the results set the foundations for exploring, developing, and
applying open-space MFMs.Comment: 16 pages, 5 figure
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