Spectral estimation of fluorescent lamps using RGB digital camera and standard color chart

Optik ◽  
2017 ◽  
Vol 130 ◽  
pp. 50-60 ◽  
Author(s):  
Jingyu Fang ◽  
Haisong Xu ◽  
Peng Xu ◽  
Zhehong Wang
Keyword(s):  
Spectral Estimation ◽  
Digital Camera ◽  
Color Chart ◽  
Fluorescent Lamps ◽  
Standard Color

scholarly journals Digital camera-based spectral estimation in open environment based on imaging condition correction

2020 ◽  
Vol 2020 (28) ◽  
pp. 347-350
Author(s):  
Jinxing Liang ◽  
Kaida Xiao
Keyword(s):  
Spectral Estimation ◽  
Estimation Method ◽  
Digital Camera ◽  
Estimation Accuracy ◽  
Adaptive Model ◽  
Imaging Condition ◽  
Practical Applications ◽  
Open Environment ◽  
Imaging Conditions ◽  
Current Stage

Digital camera-based spectral estimation in open environment is a challenge in current stage. Although some methods have been proposed in recent years, the methods do not consider the exposure inconsistency between camera spectral characterization and spectral estimation applications, that makes the proposed method cannot for practical applications. We proposed here a spectral estimation method based on imaging condition correction of which can deal with the problem exist in current methods. Using the whiteboard and raw camera response, the imaging conditions of open environment is recorded and corrected to the reference imaging conditions, and the surface spectral of object is estimated using the established spectral estimation matrix in the reference imaging conditions. The proposed method in three application models are tested and compared. The result shows that the adaptive model for imaging condition correction gives the best spectral estimation accuracy.

A Field Test for Catlinite

1970 ◽  
Vol 35 (3) ◽  
pp. 377-382 ◽  
Author(s):  
John S. Sigstad
Keyword(s):  
Field Test ◽  
Color Chart ◽  
Standard Color

AbstractThe problem of distinguishing between varieties of red pipestone from diverse sources is a complex one. The distinction between catlinite and non-catlinite can be made rather quickly, in the field, using only a streak plate and a standard color chart.

scholarly journals A Simple and Low-Cost Method to Classify Amylose Content of Rice Using a Standard Color Chart

2009 ◽  
Vol 12 (1) ◽  
pp. 97-99 ◽  
Author(s):  
Michael Ronoubigouwa Ambouroue Avaro ◽  
Ly Tong ◽  
Tomohiko Yoshida
Keyword(s):  
Amylose Content ◽  
Low Cost ◽  
Color Chart ◽  
Standard Color

Spectral estimation of tunable LED light source using digital camera based on matrix factorization

Optik ◽  
2017 ◽  
Vol 148 ◽  
pp. 90-94
Author(s):  
Jingyu Fang ◽  
Fuzheng Zhang ◽  
Haisong Xu ◽  
Zhehong Wang ◽  
Changyu Diao
Keyword(s):  
Light Source ◽  
Matrix Factorization ◽  
Spectral Estimation ◽  
Digital Camera ◽  
Led Light

Causal Probabilistic Network and Power Spectral Estimation Used in Sleep Stage Classification

1997 ◽  
Vol 36 (04/05) ◽  
pp. 41-46
Author(s):  
A. Kjaer ◽  
W. Jensen ◽  
T. Dyrby ◽  
L. Andreasen ◽  
J. Andersen ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Spectral Estimation ◽  
Interrater Agreement ◽  
Probabilistic Networks ◽  
Probabilistic Network ◽  
Spectral Bands ◽  
Power Spectral ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
The Brain

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

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