Biologically inspired high dynamic range imaging for use in machine vision /
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(Published version)
Date
2017
Authors
Griffiths, Daniel,
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
thesis
Citation
Statement of Responsibility
Conference Name
Abstract
This thesis offers contributions to the HDR imaging pipeline. These contributions are designed for machine vision applications, prioritising objective measures of signal quality, feature detectability and simplicity of implementation. A novel, Signal to Noise Ratio (SNR) derived model, for weighting traditional image stacks for HDR estimation is presented and empirically shown to improve signal quality by up to 30dB compared to the current state-of-the-art. This benefit is found primarily in the region of low irradiance, where signal quality is typically lowest.
School/Discipline
University of South Australia. School of Engineering
School of Engineering
School of Engineering
Dissertation Note
Thesis (PhD(Mechanical and Industry Engineering))--University of South Australia, 2017.
Provenance
Copyright 2017 Daniel Griffiths
Description
1 ethesis (xviii, 236 pages) :
colour illustrations, colour charts
Includes bibliographical references (pages 211-236)
colour illustrations, colour charts
Includes bibliographical references (pages 211-236)
Access Status
506 0#$fstar $2Unrestricted online access