Photometric Linearization under Near Point Light Sources

Satoshi SATO; Kazutoyo TAKATA; Kunio NOBORI

doi:10.1093/ietisy/e89-d.7.2004

Photometric Linearization under Near Point Light Sources

Satoshi SATO, Kazutoyo TAKATA, Kunio NOBORI

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We present a method for classifying image pixels of real images into multiple photometric factors: specular reflection, diffuse reflection, attached shadows and cast shadows. Conventional photometric linearization methods cannot correctly classify pixels under near point light sources, since they assume parallel light. To satisfy this assumption, our method utilizes a photometric linearization method that divides images into small regions. It also propagates linearization coefficients from neighboring regions. Our experimental results show that the proposed method can correctly classify image pixels into photometric factors, even if images are obtained under near point light sources.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2004-2011

Publication Date: 2006/07/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.7.2004

Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)

Category: Photometric Analysis

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Satoshi SATO, Kazutoyo TAKATA, Kunio NOBORI, "Photometric Linearization under Near Point Light Sources" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 7, pp. 2004-2011, July 2006, doi: 10.1093/ietisy/e89-d.7.2004.
Abstract: We present a method for classifying image pixels of real images into multiple photometric factors: specular reflection, diffuse reflection, attached shadows and cast shadows. Conventional photometric linearization methods cannot correctly classify pixels under near point light sources, since they assume parallel light. To satisfy this assumption, our method utilizes a photometric linearization method that divides images into small regions. It also propagates linearization coefficients from neighboring regions. Our experimental results show that the proposed method can correctly classify image pixels into photometric factors, even if images are obtained under near point light sources.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2004/_p

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@ARTICLE{e89-d_7_2004,
author={Satoshi SATO, Kazutoyo TAKATA, Kunio NOBORI, },
journal={IEICE TRANSACTIONS on Information},
title={Photometric Linearization under Near Point Light Sources},
year={2006},
volume={E89-D},
number={7},
pages={2004-2011},
abstract={We present a method for classifying image pixels of real images into multiple photometric factors: specular reflection, diffuse reflection, attached shadows and cast shadows. Conventional photometric linearization methods cannot correctly classify pixels under near point light sources, since they assume parallel light. To satisfy this assumption, our method utilizes a photometric linearization method that divides images into small regions. It also propagates linearization coefficients from neighboring regions. Our experimental results show that the proposed method can correctly classify image pixels into photometric factors, even if images are obtained under near point light sources.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2004},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - Photometric Linearization under Near Point Light Sources
T2 - IEICE TRANSACTIONS on Information
SP - 2004
EP - 2011
AU - Satoshi SATO
AU - Kazutoyo TAKATA
AU - Kunio NOBORI
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2004
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - We present a method for classifying image pixels of real images into multiple photometric factors: specular reflection, diffuse reflection, attached shadows and cast shadows. Conventional photometric linearization methods cannot correctly classify pixels under near point light sources, since they assume parallel light. To satisfy this assumption, our method utilizes a photometric linearization method that divides images into small regions. It also propagates linearization coefficients from neighboring regions. Our experimental results show that the proposed method can correctly classify image pixels into photometric factors, even if images are obtained under near point light sources.
ER -