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Erschienen in:
A New Scoring Method for Directional Dominance in Images Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Blur Parameter Identification Through Optimum-Path Forest

verfasst von : Rafael G. Pires, Silas E. N. Fernandes, João Paulo Papa

Erschienen in: Computer Analysis of Images and Patterns

Verlag: Springer International Publishing

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Abstract

Image acquisition processes usually add some level of noise and degradation, thus causing common problems in image restoration. The restoration process depends on the knowledge about the degradation parameters, which is critical for the image deblurring step. In order to deal with this issue, several approaches have been used in the literature, as well as techniques based on machine learning. In this paper, we presented an approach to identify blur parameters in images using the Optimum-Path Forest (OPF) classifier. Experiments demonstrated the efficiency and effectiveness of OPF when compared against some state-of-the-art pattern recognition techniques for blur parameter identification purpose, such as Support Vector Machines, Bayesian classifier and the k-nearest neighbors.

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Vorheriges Kapitel A Violence Detection Approach Based on Spatio-temporal Hypergraph Transition
Nächstes Kapitel Real-Time Human Pose Estimation via Cascaded Neural Networks Embedded with Multi-task Learning
Fußnoten
1
All these ranges for both L and \(\sigma \) were empirically chosen.
 
2
The experiments were conducted on a computer with a Pentium Intel Core i5® 650 3.2 Ghz processor, 4 GB of memory RAM and Linux Ubuntu Desktop LTS 12.04 as the operational system.
 
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Metadaten
Titel
Blur Parameter Identification Through Optimum-Path Forest
verfasst von
Rafael G. Pires
Silas E. N. Fernandes
João Paulo Papa
Copyright-Jahr
2017
Buch
Computer Analysis of Images and Patterns

Print ISBN: 978-3-319-64697-8

Electronic ISBN: 978-3-319-64698-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-64698-5_20

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