Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing

Digital images of methyl violet dye and methyl orange solutions were obtained under controlled contributions to simulate images of algal blooms. From those images, feature extraction based from both Red-Green-Blue (RGB) and Hue-Saturation-Value (HSV) color space were used. The independent variable C...

Full description

Saved in:

Bibliographic Details
Main Authors:	Uy, Roger Luis, Ilao, Joel P., Punzalan, Eric R., Ong, Marie Prane
Format:	text
Published:	Animo Repository 2014
Subjects:	Remote sensing Algal blooms Digital images Image processing Computer Sciences
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/12625
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	De La Salle University

id	oai:animorepository.dlsu.edu.ph:faculty_research-14572
record_format	eprints
spelling	oai:animorepository.dlsu.edu.ph:faculty_research-145722024-06-19T01:53:37Z Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing Uy, Roger Luis Ilao, Joel P. Punzalan, Eric R. Ong, Marie Prane Digital images of methyl violet dye and methyl orange solutions were obtained under controlled contributions to simulate images of algal blooms. From those images, feature extraction based from both Red-Green-Blue (RGB) and Hue-Saturation-Value (HSV) color space were used. The independent variable C, which is the concentration value of the dye solution, is mapped independently with the R-channel, G-channel and B-channel as well as the H-channel, S-channel and V-channel. Linear regression and non-linear regression techniques were used to determine the best fit equation while Akaike Information Criterion (AIC) were used to compare which among the equations provide the best fit. 2014-10-01T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/12625 Faculty Research Work Animo Repository Remote sensing Algal blooms Digital images Image processing Computer Sciences
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
topic	Remote sensing Algal blooms Digital images Image processing Computer Sciences
spellingShingle	Remote sensing Algal blooms Digital images Image processing Computer Sciences Uy, Roger Luis Ilao, Joel P. Punzalan, Eric R. Ong, Marie Prane Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing
description	Digital images of methyl violet dye and methyl orange solutions were obtained under controlled contributions to simulate images of algal blooms. From those images, feature extraction based from both Red-Green-Blue (RGB) and Hue-Saturation-Value (HSV) color space were used. The independent variable C, which is the concentration value of the dye solution, is mapped independently with the R-channel, G-channel and B-channel as well as the H-channel, S-channel and V-channel. Linear regression and non-linear regression techniques were used to determine the best fit equation while Akaike Information Criterion (AIC) were used to compare which among the equations provide the best fit.
format	text
author	Uy, Roger Luis Ilao, Joel P. Punzalan, Eric R. Ong, Marie Prane
author_facet	Uy, Roger Luis Ilao, Joel P. Punzalan, Eric R. Ong, Marie Prane
author_sort	Uy, Roger Luis
title	Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing
title_short	Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing
title_full	Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing
title_fullStr	Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing
title_full_unstemmed	Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing
title_sort	alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing
publisher	Animo Repository
publishDate	2014
url	https://animorepository.dlsu.edu.ph/faculty_research/12625
_version_	1806061240698535936

Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing

Similar Items