An automated self-adaptive paint mixing system using image processing

With the recent technological advancements in the field of computers and electronics, almost all tasks done by humans are now being replaced by these machines which are capable of doing the job more efficiently. In almost any field, automation is taking a giant leap which could significantly increas...

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Main Authors: Cabarle, Jose C., Bellon, James C., Evangelista, Behn Jaeson B., Reyes, Edgar Allan Q.
Format: text
Language:English
Published: Animo Repository 1995
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/7454
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Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etd_bachelors-8099
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-80992021-07-29T01:49:35Z An automated self-adaptive paint mixing system using image processing Cabarle, Jose C. Bellon, James C. Evangelista, Behn Jaeson B. Reyes, Edgar Allan Q. With the recent technological advancements in the field of computers and electronics, almost all tasks done by humans are now being replaced by these machines which are capable of doing the job more efficiently. In almost any field, automation is taking a giant leap which could significantly increase job performance and production. The project study presented here is also geared towards the abovementioned trends in the industries. The Automated Self-Adaptive Paint Mixing System uses no human effort in mixing paints. With the use of a microcomputer which would control the whole system, the paint mixing system is capable of mixing the desired paint color samples. Digital Image Processing and Neural Networks are the main theme of the study. Through the use of a video camera, paint samples are seen by the system. The images stored are processed using digital image processing. These images are used as inputs for the neural network to determine the correct paint color combination. As such, the determination of the correct paint color sample as seen by the system is achieved with the use of Neural networks with BackPropagation (BPN) as the learning technique. The microcomputer is the one responsible for controlling the amounts of paint to be mixed. 1995-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/7454 Bachelor's Theses English Animo Repository Image processing Electronic data processing--Distributed processing Paint mixing Digital electronics Electronic digital computers Automatic control
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
language English
topic Image processing
Electronic data processing--Distributed processing
Paint mixing
Digital electronics
Electronic digital computers
Automatic control
spellingShingle Image processing
Electronic data processing--Distributed processing
Paint mixing
Digital electronics
Electronic digital computers
Automatic control
Cabarle, Jose C.
Bellon, James C.
Evangelista, Behn Jaeson B.
Reyes, Edgar Allan Q.
An automated self-adaptive paint mixing system using image processing
description With the recent technological advancements in the field of computers and electronics, almost all tasks done by humans are now being replaced by these machines which are capable of doing the job more efficiently. In almost any field, automation is taking a giant leap which could significantly increase job performance and production. The project study presented here is also geared towards the abovementioned trends in the industries. The Automated Self-Adaptive Paint Mixing System uses no human effort in mixing paints. With the use of a microcomputer which would control the whole system, the paint mixing system is capable of mixing the desired paint color samples. Digital Image Processing and Neural Networks are the main theme of the study. Through the use of a video camera, paint samples are seen by the system. The images stored are processed using digital image processing. These images are used as inputs for the neural network to determine the correct paint color combination. As such, the determination of the correct paint color sample as seen by the system is achieved with the use of Neural networks with BackPropagation (BPN) as the learning technique. The microcomputer is the one responsible for controlling the amounts of paint to be mixed.
format text
author Cabarle, Jose C.
Bellon, James C.
Evangelista, Behn Jaeson B.
Reyes, Edgar Allan Q.
author_facet Cabarle, Jose C.
Bellon, James C.
Evangelista, Behn Jaeson B.
Reyes, Edgar Allan Q.
author_sort Cabarle, Jose C.
title An automated self-adaptive paint mixing system using image processing
title_short An automated self-adaptive paint mixing system using image processing
title_full An automated self-adaptive paint mixing system using image processing
title_fullStr An automated self-adaptive paint mixing system using image processing
title_full_unstemmed An automated self-adaptive paint mixing system using image processing
title_sort automated self-adaptive paint mixing system using image processing
publisher Animo Repository
publishDate 1995
url https://animorepository.dlsu.edu.ph/etd_bachelors/7454
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