7,323 research outputs found
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitations, and propose a novel MRF model that uses fully-connected
neurons to express the complex interactions among pixels. Through theoretical
analysis, we reveal an inherent connection between this model and recurrent
neural networks, and thereon derive an approximated feed-forward network that
couples multiple RNNs along opposite directions. This formulation combines the
expressive power of deep neural networks and the cyclic dependency structure of
MRF in a unified model, bringing the modeling capability to a new level. The
feed-forward approximation also allows it to be efficiently learned from data.
Experimental results on a variety of low-level vision tasks show notable
improvement over state-of-the-arts.Comment: Accepted at ECCV 201
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
Spatial process models for analyzing geostatistical data entail computations
that become prohibitive as the number of spatial locations become large. This
manuscript develops a class of highly scalable Nearest Neighbor Gaussian
Process (NNGP) models to provide fully model-based inference for large
geostatistical datasets. We establish that the NNGP is a well-defined spatial
process providing legitimate finite-dimensional Gaussian densities with sparse
precision matrices. We embed the NNGP as a sparsity-inducing prior within a
rich hierarchical modeling framework and outline how computationally efficient
Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or
decomposing large matrices. The floating point operations (flops) per iteration
of this algorithm is linear in the number of spatial locations, thereby
rendering substantial scalability. We illustrate the computational and
inferential benefits of the NNGP over competing methods using simulation
studies and also analyze forest biomass from a massive United States Forest
Inventory dataset at a scale that precludes alternative dimension-reducing
methods
Non-homogeneous polygonal Markov fields in the plane: graphical representations and geometry of higher order correlations
We consider polygonal Markov fields originally introduced by Arak and
Surgailis (1989). Our attention is focused on fields with nodes of order two,
which can be regarded as continuum ensembles of non-intersecting contours in
the plane, sharing a number of features with the two-dimensional Ising model.
We introduce non-homogeneous version of polygonal fields in anisotropic
enviroment. For these fields we provide a class of new graphical constructions
and random dynamics. These include a generalised dynamic representation,
generalised and defective disagreement loop dynamics as well as a generalised
contour birth and death dynamics. Next, we use these constructions as tools to
obtain new exact results on the geometry of higher order correlations of
polygonal Markov fields in their consistent regime.Comment: 54 page
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
We propose methodologies to train highly accurate and efficient deep
convolutional neural networks (CNNs) for image super resolution (SR). A cascade
training approach to deep learning is proposed to improve the accuracy of the
neural networks while gradually increasing the number of network layers. Next,
we explore how to improve the SR efficiency by making the network slimmer. Two
methodologies, the one-shot trimming and the cascade trimming, are proposed.
With the cascade trimming, the network's size is gradually reduced layer by
layer, without significant loss on its discriminative ability. Experiments on
benchmark image datasets show that our proposed SR network achieves the
state-of-the-art super resolution accuracy, while being more than 4 times
faster compared to existing deep super resolution networks.Comment: Accepted to IEEE Winter Conf. on Applications of Computer Vision
(WACV) 2018, Lake Tahoe, US
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