Back to articles
Articles
Volume: 2 | Article ID: art00009
Image
When to Use Linear Models for Color Calibration
Doron Sherman
Joyce E. Farrell
  DOI :  10.2352/CIC.1994.2.1.art00009  Published Online :  January 1994
Abstract

With the advent of vector-space approaches, linear estimation techniques can be used in various ways for different imaging scenarios. We compare two methods for constructing linear models for surface reflectance spectra, the Principal-Components Analysis (PCA) and the One-Mode Analysis (OMA) applied in simulating image capture under a number of realistic lighting conditions. We demonstrate that successfully using such methods depends on the exact problem at hand.

Journal Title : Color and Imaging Conference
Publisher Name : Society of Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151, USA
Subject Areas :
Views 7
Downloads 0
 DOI 10.2352/CIC.1994.2.1.art00009
 articleview.views 7
 articleview.downloads 0
  Cite this article 

Doron Sherman, Joyce E. Farrell, "When to Use Linear Models for Color Calibration"  in Proc. IS&T 2nd Color and Imaging Conf.,  1994,  pp 33 - 36,  https://doi.org/10.2352/CIC.1994.2.1.art00009

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 1994
72010350
Color and Imaging Conference
color imaging conf
2166-9635
Society of Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151, USA
2166-9635(19940101)1994:1L.33;1-
cic_v1994n1/splitsection9.xml
/ist/cic/1994/00001994/00000001/art00009
Articles
When to Use Linear Models for Color Calibration
ShermanDoron
FarrellJoyce E.
01011994
1994
1
33
36
1994
With the advent of vector-space approaches, linear estimation techniques can be used in various ways for different imaging scenarios. We compare two methods for constructing linear models for surface reflectance spectra, the Principal-Components Analysis (PCA) and the One-Mode Analysis (OMA) applied in simulating image capture under a number of realistic lighting conditions. We demonstrate that successfully using such methods depends on the exact problem at hand.