45,600 research outputs found
Hierarchical Object Parsing from Structured Noisy Point Clouds
Object parsing and segmentation from point clouds are challenging tasks
because the relevant data is available only as thin structures along object
boundaries or other features, and is corrupted by large amounts of noise. To
handle this kind of data, flexible shape models are desired that can accurately
follow the object boundaries. Popular models such as Active Shape and Active
Appearance models lack the necessary flexibility for this task, while recent
approaches such as the Recursive Compositional Models make model
simplifications in order to obtain computational guarantees. This paper
investigates a hierarchical Bayesian model of shape and appearance in a
generative setting. The input data is explained by an object parsing layer,
which is a deformation of a hidden PCA shape model with Gaussian prior. The
paper also introduces a novel efficient inference algorithm that uses informed
data-driven proposals to initialize local searches for the hidden variables.
Applied to the problem of object parsing from structured point clouds such as
edge detection images, the proposed approach obtains state of the art parsing
errors on two standard datasets without using any intensity information.Comment: 13 pages, 16 figure
A Unified Framework for Compositional Fitting of Active Appearance Models
Active Appearance Models (AAMs) are one of the most popular and
well-established techniques for modeling deformable objects in computer vision.
In this paper, we study the problem of fitting AAMs using Compositional
Gradient Descent (CGD) algorithms. We present a unified and complete view of
these algorithms and classify them with respect to three main characteristics:
i) cost function; ii) type of composition; and iii) optimization method.
Furthermore, we extend the previous view by: a) proposing a novel Bayesian cost
function that can be interpreted as a general probabilistic formulation of the
well-known project-out loss; b) introducing two new types of composition,
asymmetric and bidirectional, that combine the gradients of both image and
appearance model to derive better conver- gent and more robust CGD algorithms;
and c) providing new valuable insights into existent CGD algorithms by
reinterpreting them as direct applications of the Schur complement and the
Wiberg method. Finally, in order to encourage open research and facilitate
future comparisons with our work, we make the implementa- tion of the
algorithms studied in this paper publicly available as part of the Menpo
Project.Comment: 39 page
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next observation to make on the objective function, which must take into
account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling areas likely to offer improvement over the current best
observation). We also present two detailed extensions of Bayesian optimization,
with experiments---active user modelling with preferences, and hierarchical
reinforcement learning---and a discussion of the pros and cons of Bayesian
optimization based on our experiences
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
The idea of computer vision as the Bayesian inverse problem to computer
graphics has a long history and an appealing elegance, but it has proved
difficult to directly implement. Instead, most vision tasks are approached via
complex bottom-up processing pipelines. Here we show that it is possible to
write short, simple probabilistic graphics programs that define flexible
generative models and to automatically invert them to interpret real-world
images. Generative probabilistic graphics programs consist of a stochastic
scene generator, a renderer based on graphics software, a stochastic likelihood
model linking the renderer's output and the data, and latent variables that
adjust the fidelity of the renderer and the tolerance of the likelihood model.
Representations and algorithms from computer graphics, originally designed to
produce high-quality images, are instead used as the deterministic backbone for
highly approximate and stochastic generative models. This formulation combines
probabilistic programming, computer graphics, and approximate Bayesian
computation, and depends only on general-purpose, automatic inference
techniques. We describe two applications: reading sequences of degraded and
adversarially obscured alphanumeric characters, and inferring 3D road models
from vehicle-mounted camera images. Each of the probabilistic graphics programs
we present relies on under 20 lines of probabilistic code, and supports
accurate, approximately Bayesian inferences about ambiguous real-world images.Comment: The first two authors contributed equally to this wor
Statistical tests of sterile neutrinos using cosmology and short-baseline data
In this paper we revisit the question of the information which cosmology
provides on the scenarios with sterile neutrinos invoked to describe the SBL
anomalies using Bayesian statistical tests. We perform an analysis of the
cosmological data in CDM cosmologies for different
cosmological data combinations, and obtain the marginalized cosmological
likelihood in terms of the two relevant parameters, the sterile neutrino mass
and its contribution to the energy density of the early Universe . We then present an analysis to quantify at which level a model with one
sterile neutrino is (dis)favoured with respect to a model with only three
active neutrinos, using results from both short-baseline experiments and
cosmology. We study the dependence of the results on the cosmological data
considered, in particular on the inclusion of the recent BICEP2 results and the
SZ cluster data from the Planck mission. We find that only when the cluster
data is included the model with one extra sterile neutrino can become more
favoured that the model with only the three active ones provided the sterile
neutrino contribution to radiation density is suppressed with respect to the
fully thermalized scenario. We have also quantified the level of
(in)compatibility between the sterile neutrino masses implied by the
cosmological and SBL results.Comment: 23 pages, 4 figure
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