Multi-Modal Non-Isotropic Light Source Modelling for Reflectance Estimation in Hyperspectral Imaging

Mehami, J; Falque, R; Vidal-Calleja, T; Alempijevic, A

Multi-Modal Non-Isotropic Light Source Modelling for Reflectance Estimation in Hyperspectral Imaging

Mehami, J Falque, R Vidal-Calleja, T Alempijevic, A

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2022, 7, (4), pp. 10336-10343
Issue Date:: 2022-10-01

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Field	Value	Language
dc.contributor.author	Mehami, J
dc.contributor.author	Falque, R
dc.contributor.author	Vidal-Calleja, T
dc.contributor.author	Alempijevic, A https://orcid.org/0000-0002-1769-8041
dc.date.accessioned	2023-01-10T01:11:24Z
dc.date.available	2023-01-10T01:11:24Z
dc.date.issued	2022-10-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2022, 7, (4), pp. 10336-10343
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/164797
dc.description.abstract	Estimating reflectance is key when working with hyperspectral cameras. The modelling of light sources can aid reflectance estimation, however, it is commonly overlooked. The key contribution of this letter is a physics-based, data-driven model formed by a Gaussian Process (GP) with a unique mean function capable of modelling a light source with an asymmetric radiant intensity distribution (RID) and a configurable attenuation function. This is referred to as the light-source-mean model. Moreover, we argue that by utilising multi-modal sensing information, we can achieve improved reflectance estimation using the proposed light source model with shape information obtained by depth cameras. An existing reflectance estimation method, that solves the dichromatic reflectance model (DRM) via quadratic programming optimisation, is augmented with terms that allow input of shape information. Experiments in simulation show that the light-source-mean GP model had less error when compared to a parametric model. The improved reflectance estimation outperforms existing methods in simulation by reducing the error by 96.8% on average when compared to existing works. We further validate the improved reflectance estimation method through a multi-modal classification application.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Robotics and Automation Letters
dc.relation.isbasedon	10.1109/LRA.2022.3192208
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0913 Mechanical Engineering
dc.title	Multi-Modal Non-Isotropic Light Source Modelling for Reflectance Estimation in Hyperspectral Imaging
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-07-19T00:00:00+1000Z
dc.date.updated	2023-01-10T01:11:20Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	4

Abstract:

Estimating reflectance is key when working with hyperspectral cameras. The modelling of light sources can aid reflectance estimation, however, it is commonly overlooked. The key contribution of this letter is a physics-based, data-driven model formed by a Gaussian Process (GP) with a unique mean function capable of modelling a light source with an asymmetric radiant intensity distribution (RID) and a configurable attenuation function. This is referred to as the light-source-mean model. Moreover, we argue that by utilising multi-modal sensing information, we can achieve improved reflectance estimation using the proposed light source model with shape information obtained by depth cameras. An existing reflectance estimation method, that solves the dichromatic reflectance model (DRM) via quadratic programming optimisation, is augmented with terms that allow input of shape information. Experiments in simulation show that the light-source-mean GP model had less error when compared to a parametric model. The improved reflectance estimation outperforms existing methods in simulation by reducing the error by 96.8% on average when compared to existing works. We further validate the improved reflectance estimation method through a multi-modal classification application.

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http://hdl.handle.net/10453/164797