Estimating Reflectance Parameters, Light Direction, and Shape From a Single Multispectral Image

Rahman, S; Robles-Kelly, A

Estimating Reflectance Parameters, Light Direction, and Shape From a Single Multispectral Image

Rahman, S

Robles-Kelly, A

Permalink

Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computational Imaging, 2017, 3 (4), pp. 837 - 852
Issue Date:: 2017-12

Closed Access

	Filename	Description	Size
	07953651.pdf	Published Version	1.59 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Rahman, S https://orcid.org/0000-0001-6226-2434	en_US
dc.contributor.author	Robles-Kelly, A	en_US
dc.date.issued	2017-12	en_US
dc.identifier.citation	IEEE Transactions on Computational Imaging, 2017, 3 (4), pp. 837 - 852	en_US
dc.identifier.issn	2333-9403	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125744
dc.description.abstract	This paper presents a novel approach for estimating the light direction, shape, and reflectance parameters from a single multispectral image. We start from a general formulation that hinges in the notion that the light reflected from an object can be deemed to be a linear combination of specular and diffuse reflections. This permits the recovery of the reflection parameters through an iterative optimization scheme, which we render well posed by adopting a novel reparameterization that reduces the number of degrees of freedom in the cost function. With the estimated specular reflectance parameters, we recover the single point light source position from specular highlights by applying two novel constraints, coplanarity and Kullback-Leibler divergence. Then, by integrating the knowledge of light source and diffuse reflectance parameters, we recover shape of the scene from the diffuse component. Our approach is quite general in nature and can be applied to a family of reflectance models that are based on the Fresnel reflection theory. We demonstrate the utility of our method on synthetic and real world imagery. We also compare our results to several alternatives in the literature.	en_US
dc.publisher	Institute of Electrical and Electronics Engineers	en_US
dc.relation.ispartof	IEEE Transactions on Computational Imaging	en_US
dc.relation.isbasedon	10.1109/TCI.2017.2717788	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Estimating Reflectance Parameters, Light Direction, and Shape From a Single Multispectral Image	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	3	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
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	closed_access	*
pubs.consider-herdc	true	en_US
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

This paper presents a novel approach for estimating the light direction, shape, and reflectance parameters from a single multispectral image. We start from a general formulation that hinges in the notion that the light reflected from an object can be deemed to be a linear combination of specular and diffuse reflections. This permits the recovery of the reflection parameters through an iterative optimization scheme, which we render well posed by adopting a novel reparameterization that reduces the number of degrees of freedom in the cost function. With the estimated specular reflectance parameters, we recover the single point light source position from specular highlights by applying two novel constraints, coplanarity and Kullback-Leibler divergence. Then, by integrating the knowledge of light source and diffuse reflectance parameters, we recover shape of the scene from the diffuse component. Our approach is quite general in nature and can be applied to a family of reflectance models that are based on the Fresnel reflection theory. We demonstrate the utility of our method on synthetic and real world imagery. We also compare our results to several alternatives in the literature.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/125744