Sparse Bayesian Learning Of Filters For Efficient Image Expansion

Image Processing- Dotnet

We propose a framework for expanding a given image using an interpolator that is trained in advance with training data, based on sparse Bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones. ....etc

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Mining Web Graphs for Recommendations



INTRODUCTION
With the diverse and explosive growth of Web information,
how to organize and utilize the information effectively and
efficiently has become more and more critical. This is especially
important for Web 2.0 related applications since usergenerated
information is more free-style and less structured,
which increases the difficulties in mining useful information
from these data sources. In order to satisfy the information
nee ....etc

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Abstract In image processing, restoration is expected to improve
the qualitative inspection of the image and the performance
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of the nonlocal (NL)-means filter is proposed for ....etc

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Abstract.
We present a simple way of designing deterministic algorithms for problems in the data stream model via lossless expander graphs.We illustrate this by considering two problems, namely, k-sparsity testing and estimating frequency of items.
1 Introduction
We say that an n-dimensional vector f from Zn is k-sparse if it has at most k non-zero entries. The problem is to test whether f is k-sparse or no after it has been subject to a sequence of coordinate wise updates in arbitrary order, that is, f is the frequency vect ....etc

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Abstract Security risk assessment and mitigation are two vital processes that need to be executed to maintain a productive IT infrastructure. On one hand, models such as attack graphs and attack trees have been proposed to assess the cause-consequence relationships between various network states, while on the other hand, different decision problems have been explored to identify the minimum-cost hardening measures. However, these risk models do not help reason about the causal dependencies between network states. Further, the optimization form ....etc

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Bayesian Networks are becoming an increasingly important area for research and application in the entire field of Artificial Intelligence. This paper explores the nature and implications for Bayesian Networks beginning with an n overview and comparison of inferential statistics and Bayes theorem. The nature, relevance and applicability for Bayesian Network theories for advanced computability form the core of the current discussion.

A number of current applications using Bayesian networks are examined. The paper concludes with a brief descri ....etc

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