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Focusing on out-of-focus: assessing defocus estimation algorithms for the benefit of automated image masking

Geert Verhoeven UGent (2018) ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLII-2. p.1149-1156
abstract
Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the " sharpness " of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted " acceptably " sharp throughout the whole image collection. Although none of these three fields has ever properly quantified " acceptably sharp " , it is more or less standard practice to mask those image portions that appear to be unsharp due to the limited depth of field around the plane of focus (whether this means blurry object parts or completely out-of-focus backgrounds). This paper will assess how well-or ill-suited defocus estimating algorithms are for automatically masking a series of photographs, since this could speed up modelling pipelines with many hundreds or thousands of photographs. To that end, the paper uses five different real-world datasets and compares the output of three state-of-the-art edge-based defocus estimators. Afterwards, critical comments and plans for the future finalise this paper.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Automated masking, Defocus estimation, Depth of field, Edge extraction, Image-based modelling, Out-of-focus blur
journal title
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
volume
XLII-2
pages
8 pages
publisher
Copernicus GmbH
conference name
ISPRS TC II Mid-term Symposium Towards Photogrammetry 2020
conference organizer
ISPRS
conference location
Riva Del Garda
conference start
2018-06-04
conference end
2018-06-07
ISSN
2194-9034
DOI
10.5194/isprs-archives-xlii-2-1149-2018
language
English
UGent publication?
yes
classification
U
copyright statement
I have retained and own the full copyright for this publication
id
8563824
handle
http://hdl.handle.net/1854/LU-8563824
date created
2018-06-01 08:24:15
date last changed
2018-06-28 11:20:41
@article{8563824,
  abstract     = {Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the {\textacutedbl} sharpness {\textacutedbl} of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted {\textacutedbl} acceptably {\textacutedbl} sharp throughout the whole image collection. Although none of these three fields has ever properly quantified {\textacutedbl} acceptably sharp {\textacutedbl} , it is more or less standard practice to mask those image portions that appear to be unsharp due to the limited depth of field around the plane of focus (whether this means blurry object parts or completely out-of-focus backgrounds). This paper will assess how well-or ill-suited defocus estimating algorithms are for automatically masking a series of photographs, since this could speed up modelling pipelines with many hundreds or thousands of photographs. To that end, the paper uses five different real-world datasets and compares the output of three state-of-the-art edge-based defocus estimators. Afterwards, critical comments and plans for the future finalise this paper.},
  author       = {Verhoeven, Geert},
  issn         = {2194-9034},
  journal      = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  keyword      = {Automated masking,Defocus estimation,Depth of field,Edge extraction,Image-based modelling,Out-of-focus blur},
  language     = {eng},
  location     = {Riva Del Garda},
  pages        = {1149--1156},
  publisher    = {Copernicus GmbH},
  title        = {Focusing on out-of-focus: assessing defocus estimation algorithms for the benefit of automated image masking},
  url          = {http://dx.doi.org/10.5194/isprs-archives-xlii-2-1149-2018},
  volume       = {XLII-2},
  year         = {2018},
}

Chicago
Verhoeven, Geert. 2018. “Focusing on Out-of-focus: Assessing Defocus Estimation Algorithms for the Benefit of Automated Image Masking.” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2: 1149–1156.
APA
Verhoeven, Geert. (2018). Focusing on out-of-focus: assessing defocus estimation algorithms for the benefit of automated image masking. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2, 1149–1156. Presented at the ISPRS TC II Mid-term Symposium Towards Photogrammetry 2020.
Vancouver
1.
Verhoeven G. Focusing on out-of-focus: assessing defocus estimation algorithms for the benefit of automated image masking. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Copernicus GmbH; 2018;XLII-2:1149–56.
MLA
Verhoeven, Geert. “Focusing on Out-of-focus: Assessing Defocus Estimation Algorithms for the Benefit of Automated Image Masking.” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (2018): 1149–1156. Print.