56 research outputs found
A survey of exemplar-based texture synthesis
Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statistical signature; then, a random sampling conditioned
to this signature produces genuinely different texture images. The second class
boils down to a clever "copy-paste" procedure, which stitches together large
regions of the sample. Hybrid methods try to combine ideas from both approaches
to avoid their hurdles. The recent approaches using convolutional neural
networks fit to this classification, some being statistical and others
performing patch re-arrangement in the feature space. They produce impressive
synthesis on various kinds of textures. Nevertheless, we found that most real
textures are organized at multiple scales, with global structures revealed at
coarse scales and highly varying details at finer ones. Thus, when confronted
with large natural images of textures the results of state-of-the-art methods
degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe
FRAME. New method presented: CNNMR
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
This study investigates the effects of Markov chain Monte Carlo (MCMC)
sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is
restricted to the family of unnormalized probability densities for which the
negative log density (or energy function) is a ConvNet. We find that many of
the techniques used to stabilize training in previous studies are not
necessary. ML learning with a ConvNet potential requires only a few
hyper-parameters and no regularization. Using this minimal framework, we
identify a variety of ML learning outcomes that depend solely on the
implementation of MCMC sampling.
On one hand, we show that it is easy to train an energy-based model which can
sample realistic images with short-run Langevin. ML can be effective and stable
even when MCMC samples have much higher energy than true steady-state samples
throughout training. Based on this insight, we introduce an ML method with
purely noise-initialized MCMC, high-quality short-run synthesis, and the same
budget as ML with informative MCMC initialization such as CD or PCD. Unlike
previous models, our energy model can obtain realistic high-diversity samples
from a noise signal after training.
On the other hand, ConvNet potentials learned with non-convergent MCMC do not
have a valid steady-state and cannot be considered approximate unnormalized
densities of the training data because long-run MCMC samples differ greatly
from observed images. We show that it is much harder to train a ConvNet
potential to learn a steady-state over realistic images. To our knowledge,
long-run MCMC samples of all previous models lose the realism of short-run
samples. With correct tuning of Langevin noise, we train the first ConvNet
potentials for which long-run and steady-state MCMC samples are realistic
images.Comment: Code available at: https://github.com/point0bar1/ebm-anatom
Wavelet-based Texture Model for Crowd Dynamic Analysis
Crowd event detection techniques aim at solving
real-world surveillance problems, such as detecting crowd
anomaly and tracking specific person in a highly dynamic
crowd scene. In this paper, we proposed an innovate
texture-based analysis method to model crowd dynamics
and us it to distinguish the crowd behaviours. To describe
complicated crowd scenes, homogeneous random features
have been deployed in the research for behavioural template
matching. Experiment results have shown that the anomaly
appearing in crowd scenes can be effectively and efficiently
identified by using the devised methods
Estimation de paramètres de champs markoviens cachés avec applications à la segmentation d'images et la localisation de formes
Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal
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