44,922 research outputs found
Markov Random Field Models: A Bayesian Approach to Computer Vision Problems
The object of our study is the Bayesian approach in solving computer vision problems. We examine in particular: (i) applications of Markov random field (MRF) models to modeling spatial images; (ii) MRF based statistical methods for image restoration, segmentation, texture modeling and integration of different visual cues
Conditional Random Fields for Image Labeling
With the rapid development and application of CRFs (Conditional Random Fields) in computer vision, many researchers have made some outstanding progress in this domain because CRFs solve the classical version of the label bias problem with respect to MEMMs (maximum entropy Markov models) and HMMs (hidden Markov models). This paper reviews the research development and status of object recognition with CRFs and especially introduces two main discrete optimization methods for image labeling with CRFs: graph cut and mean field approximation. This paper describes graph cut briefly while it introduces mean field approximation more detailedly which has a substantial speed of inference and is researched popularly in recent years
Quantum Annealing for Computer Vision Minimization Problems
Computer Vision (CV) labelling algorithms play a pivotal role in the domain
of low-level vision. For decades, it has been known that these problems can be
elegantly formulated as discrete energy minimization problems derived from
probabilistic graphical models (such as Markov Random Fields). Despite recent
advances in inference algorithms (such as graph-cut and message-passing
algorithms), the resulting energy minimization problems are generally viewed as
intractable. The emergence of quantum computations, which offer the potential
for faster solutions to certain problems than classical methods, has led to an
increased interest in utilizing quantum properties to overcome intractable
problems. Recently, there has also been a growing interest in Quantum Computer
Vision (QCV), with the hope of providing a credible alternative or assistant to
deep learning solutions in the field. This study investigates a new Quantum
Annealing based inference algorithm for CV discrete energy minimization
problems. Our contribution is focused on Stereo Matching as a significant CV
labeling problem. As a proof of concept, we also use a hybrid quantum-classical
solver provided by D-Wave System to compare our results with the best classical
inference algorithms in the literature
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
Investigation of commuting Hamiltonian in quantum Markov network
Graphical Models have various applications in science and engineering which
include physics, bioinformatics, telecommunication and etc. Usage of graphical
models needs complex computations in order to evaluation of marginal
functions,so there are some powerful methods including mean field
approximation, belief propagation algorithm and etc. Quantum graphical models
have been recently developed in context of quantum information and computation,
and quantum statistical physics, which is possible by generalization of
classical probability theory to quantum theory. The main goal of this paper is
preparing a primary generalization of Markov network, as a type of graphical
models, to quantum case and applying in quantum statistical physics.We have
investigated the Markov network and the role of commuting Hamiltonian terms in
conditional independence with simple examples of quantum statistical physics.Comment: 11 pages, 8 figure
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