3,288 research outputs found
Shadow Computations using Robust Epsilon Visibility
Analytic visibility algorithms, for example methods which compute a subdivided mesh to represent shadows, are notoriously unrobust and hard to use in practice. We present a new method based on a generalized definition of extremal stabbing lines, which are the extremities of shadow boundaries. We treat scenes containing multiple edges or vertices in degenerate configurations, (e.g., collinear or coplanar). We introduce a robust epsilon method to determine whether each generalized extremal stabbing line is blocked, or is touched by these scene elements, and thus added to the line's generators. We develop robust blocker predicates for polygons which are smaller than epsilon. For larger values, small shadow features merge and eventually disappear. We can thus robustly connect generalized extremal stabbing lines in degenerate scenes to form shadow boundaries. We show that our approach is consistent, and that shadow boundary connectivity is preserved when features merge. We have implemented our algorithm, and show that we can robustly compute analytic shadow boundaries to the precision of our chosen epsilon threshold for non-trivial models, containing numerous degeneracies
On the Design and Analysis of Multiple View Descriptors
We propose an extension of popular descriptors based on gradient orientation
histograms (HOG, computed in a single image) to multiple views. It hinges on
interpreting HOG as a conditional density in the space of sampled images, where
the effects of nuisance factors such as viewpoint and illumination are
marginalized. However, such marginalization is performed with respect to a very
coarse approximation of the underlying distribution. Our extension leverages on
the fact that multiple views of the same scene allow separating intrinsic from
nuisance variability, and thus afford better marginalization of the latter. The
result is a descriptor that has the same complexity of single-view HOG, and can
be compared in the same manner, but exploits multiple views to better trade off
insensitivity to nuisance variability with specificity to intrinsic
variability. We also introduce a novel multi-view wide-baseline matching
dataset, consisting of a mixture of real and synthetic objects with ground
truthed camera motion and dense three-dimensional geometry
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
The role of homophily in the emergence of opinion controversies
Understanding the emergence of strong controversial issues in modern
societies is a key issue in opinion studies. A commonly diffused idea is the
fact that the increasing of homophily in social networks, due to the modern
ICT, can be a driving force for opinion polariation. In this paper we address
the problem with a modelling approach following three basic steps. We first
introduce a network morphogenesis model to reconstruct network structures where
homophily can be tuned with a parameter. We show that as homophily increases
the emergence of marked topological community structures in the networks
raises. Secondly, we perform an opinion dynamics process on homophily dependent
networks and we show that, contrary to the common idea, homophily helps
consensus formation. Finally, we introduce a tunable external media pressure
and we show that, actually, the combination of homophily and media makes the
media effect less effective and leads to strongly polarized opinion clusters.Comment: 24 pages, 10 figure
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning
With the advent of the Internet of Things (IoT), an increasing number of
energy harvesting methods are being used to supplement or supplant battery
based sensors. Energy harvesting sensors need to be configured according to the
application, hardware, and environmental conditions to maximize their
usefulness. As of today, the configuration of sensors is either manual or
heuristics based, requiring valuable domain expertise. Reinforcement learning
(RL) is a promising approach to automate configuration and efficiently scale
IoT deployments, but it is not yet adopted in practice. We propose solutions to
bridge this gap: reduce the training phase of RL so that nodes are operational
within a short time after deployment and reduce the computational requirements
to scale to large deployments. We focus on configuration of the sampling rate
of indoor solar panel based energy harvesting sensors. We created a simulator
based on 3 months of data collected from 5 sensor nodes subject to different
lighting conditions. Our simulation results show that RL can effectively learn
energy availability patterns and configure the sampling rate of the sensor
nodes to maximize the sensing data while ensuring that energy storage is not
depleted. The nodes can be operational within the first day by using our
methods. We show that it is possible to reduce the number of RL policies by
using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure
An Upper Bound on the Average Size of Silhouettes
It is a widely observed phenomenon in computer graphics that the size of the
silhouette of a polyhedron is much smaller than the size of the whole
polyhedron. This paper provides, for the first time, theoretical evidence
supporting this for a large class of objects, namely for polyhedra that
approximate surfaces in some reasonable way; the surfaces may be non-convex and
non-differentiable and they may have boundaries. We prove that such polyhedra
have silhouettes of expected size where the average is taken over
all points of view and n is the complexity of the polyhedron
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