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

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...

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Bibliographic Details
Main Authors: Uy, Roger Luis, Ilao, Joel P., Punzalan, Eric, Ong, Mariel Prane
Format: text
Published: Animo Repository 2014
Subjects:
Dyes and dyeing—Computer simulation
Algal blooms—Computer simulation
Image processing—Digital techniques
Computer Sciences
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/327
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Institution: De La Salle University
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Summary: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 IEEE.

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