J�rgen D. Ernst
High-sensitivity analysis of polarization by surface reflection
Ernst, J�rgen D.; Atkinson, Gary
Abstract
© 2018, The Author(s). The exploitation of polarization information in the field of computer vision has become progressively more popular during the last few decades. This is primarily due to (1) the fact that polarization is a source of mostly untapped information for machine vision; (2) the relative computational ease by which geometrical information about a scene (e.g.surface normals) may be extracted from polarization data; and (3) the recent introduction of camera hardware able to capture polarization data in real time. The motivation for this paper is that a detailed quantitative study into the precision of polarization measurements with respect to expectation has yet to be performed. The paper therefore presents a detailed analysis and optimization of the key aspects of data capture necessary to acquire the most precise (as opposed to fast) results for the benefit of future research into the field of “polarization vision.” The paper mainly focuses on a rotating polarizer method as this is shown to be the most accurate for high-sensitivity measurements. Commercial polarization cameras by contrast generally sacrifice precision for the benefit of much shorter capture times. That said, the paper reviews the state of the art in polarization camera technology and quantitatively evaluates the performance of one such camera: the Fraunhofer “POLKA.”.
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 2, 2018 |
Online Publication Date | Aug 3, 2018 |
Publication Date | Oct 1, 2018 |
Deposit Date | Jul 9, 2018 |
Publicly Available Date | Jul 9, 2018 |
Journal | Machine Vision and Applications |
Print ISSN | 0932-8092 |
Electronic ISSN | 1432-1769 |
Publisher | Springer Verlag |
Peer Reviewed | Peer Reviewed |
Volume | 29 |
Issue | 7 |
Pages | 1171-1189 |
DOI | https://doi.org/10.1007/s00138-018-0962-7 |
Keywords | polarization, physics-based reflectance modelling, shape analysis |
Public URL | https://uwe-repository.worktribe.com/output/878163 |
Publisher URL | https://doi.org/10.1007/s00138-018-0962-7 |
Additional Information | Additional Information : The dataset for this study is available from the UWE Research Data Repository: http://researchdata.uwe.ac.uk/244. |
Contract Date | Jul 9, 2018 |
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