11,426 research outputs found
Joint Material and Illumination Estimation from Photo Sets in the Wild
Faithful manipulation of shape, material, and illumination in 2D Internet
images would greatly benefit from a reliable factorization of appearance into
material (i.e., diffuse and specular) and illumination (i.e., environment
maps). On the one hand, current methods that produce very high fidelity
results, typically require controlled settings, expensive devices, or
significant manual effort. To the other hand, methods that are automatic and
work on 'in the wild' Internet images, often extract only low-frequency
lighting or diffuse materials. In this work, we propose to make use of a set of
photographs in order to jointly estimate the non-diffuse materials and sharp
lighting in an uncontrolled setting. Our key observation is that seeing
multiple instances of the same material under different illumination (i.e.,
environment), and different materials under the same illumination provide
valuable constraints that can be exploited to yield a high-quality solution
(i.e., specular materials and environment illumination) for all the observed
materials and environments. Similar constraints also arise when observing
multiple materials in a single environment, or a single material across
multiple environments. The core of this approach is an optimization procedure
that uses two neural networks that are trained on synthetic images to predict
good gradients in parametric space given observation of reflected light. We
evaluate our method on a range of synthetic and real examples to generate
high-quality estimates, qualitatively compare our results against
state-of-the-art alternatives via a user study, and demonstrate
photo-consistent image manipulation that is otherwise very challenging to
achieve
Solving Set Cover with Pairs Problem using Quantum Annealing
Here we consider using quantum annealing to solve Set Cover with Pairs (SCP), an NP-hard combinatorial optimization problem that plays an important role in networking, computational biology, and biochemistry. We show an explicit construction of Ising Hamiltonians whose ground states encode the solution of SCP instances. We numerically simulate the time-dependent Schrödinger equation in order to test the performance of quantum annealing for random instances and compare with that of simulated annealing. We also discuss explicit embedding strategies for realizing our Hamiltonian construction on the D-wave type restricted Ising Hamiltonian based on Chimera graphs. Our embedding on the Chimera graph preserves the structure of the original SCP instance and in particular, the embedding for general complete bipartite graphs and logical disjunctions may be of broader use than that the specific problem we deal with
Rewriting Constraint Models with Metamodels
An important challenge in constraint programming is to rewrite constraint
models into executable programs calculat- ing the solutions. This phase of
constraint processing may require translations between constraint programming
lan- guages, transformations of constraint representations, model
optimizations, and tuning of solving strategies. In this paper, we introduce a
pivot metamodel describing the common fea- tures of constraint models including
different kinds of con- straints, statements like conditionals and loops, and
other first-class elements like object classes and predicates. This metamodel
is general enough to cope with the constructions of many languages, from
object-oriented modeling languages to logic languages, but it is independent
from them. The rewriting operations manipulate metamodel instances apart from
languages. As a consequence, the rewriting operations apply whatever languages
are selected and they are able to manage model semantic information. A bridge
is created between the metamodel space and languages using parsing techniques.
Tools from the software engineering world can be useful to implement this
framework
Spotlight the Negatives: A Generalized Discriminative Latent Model
Discriminative latent variable models (LVM) are frequently applied to various
visual recognition tasks. In these systems the latent (hidden) variables
provide a formalism for modeling structured variation of visual features.
Conventionally, latent variables are de- fined on the variation of the
foreground (positive) class. In this work we augment LVMs to include negative
latent variables corresponding to the background class. We formalize the
scoring function of such a generalized LVM (GLVM). Then we discuss a framework
for learning a model based on the GLVM scoring function. We theoretically
showcase how some of the current visual recognition methods can benefit from
this generalization. Finally, we experiment on a generalized form of Deformable
Part Models with negative latent variables and show significant improvements on
two different detection tasks.Comment: Published in proceedings of BMVC 201
Probabilistic Graphical Models on Multi-Core CPUs using Java 8
In this paper, we discuss software design issues related to the development
of parallel computational intelligence algorithms on multi-core CPUs, using the
new Java 8 functional programming features. In particular, we focus on
probabilistic graphical models (PGMs) and present the parallelisation of a
collection of algorithms that deal with inference and learning of PGMs from
data. Namely, maximum likelihood estimation, importance sampling, and greedy
search for solving combinatorial optimisation problems. Through these concrete
examples, we tackle the problem of defining efficient data structures for PGMs
and parallel processing of same-size batches of data sets using Java 8
features. We also provide straightforward techniques to code parallel
algorithms that seamlessly exploit multi-core processors. The experimental
analysis, carried out using our open source AMIDST (Analysis of MassIve Data
STreams) Java toolbox, shows the merits of the proposed solutions.Comment: Pre-print version of the paper presented in the special issue on
Computational Intelligence Software at IEEE Computational Intelligence
Magazine journa
- …