A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.

Библиографические подробности
Главные авторы:	Mahmud, Shakib Izaz, Shovon, Sartaz Islam, Hasnat, Md. Abrar, Na s, Md. Fahim
Другие авторы:	Alam, Md. Ashraful
Формат:	Диссертация
Язык:	English
Опубликовано:	Brac University 2022
Предметы:	Auto Encoded Techniques Reflection Co efficient Neural networks Deep learning KNN ANN Random forest Logistic regression Naive bayes Decision tree Digital Illuminance meter Cognitive learning theory (Deep learning) Neural networks (Computer science)
Online-ссылка:	http://hdl.handle.net/10361/17125

id	10361-17125
record_format	dspace
spelling	10361-171252022-08-28T21:01:34Z A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks Mahmud, Shakib Izaz Shovon, Sartaz Islam Hasnat, Md. Abrar Na s, Md. Fahim Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Auto Encoded Techniques Reflection Co efficient Neural networks Deep learning KNN ANN Random forest Logistic regression Naive bayes Decision tree Digital Illuminance meter Cognitive learning theory (Deep learning) Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 46-48). Color vision approach using auto encoded technique is an effective way to detect objects. This approach considers various factors like movement detection, size and shape detection, color detection etc. Here we have considered reflection co efficient as another parameter to detect object material in different ambient lighting conditions. We are proposing to use deep learning methods to train our AI from values of light intensity of different objects in many controlled environments using digital illuminance meter also deep learning architecture on image data for detecting surface reflectance. Shakib Izaz Mahmud Md. Abrar Hasnat Sartaz Islam Shovon Md. Fahim Na s B. Computer Science 2022-08-28T08:38:47Z 2022-08-28T08:38:47Z 2021 2021-09 Thesis ID 17101269 ID 17101178 ID 17101276 ID 17101171 http://hdl.handle.net/10361/17125 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Auto Encoded Techniques Reflection Co efficient Neural networks Deep learning KNN ANN Random forest Logistic regression Naive bayes Decision tree Digital Illuminance meter Cognitive learning theory (Deep learning) Neural networks (Computer science)
spellingShingle	Auto Encoded Techniques Reflection Co efficient Neural networks Deep learning KNN ANN Random forest Logistic regression Naive bayes Decision tree Digital Illuminance meter Cognitive learning theory (Deep learning) Neural networks (Computer science) Mahmud, Shakib Izaz Shovon, Sartaz Islam Hasnat, Md. Abrar Na s, Md. Fahim A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.
author2	Alam, Md. Ashraful
author_facet	Alam, Md. Ashraful Mahmud, Shakib Izaz Shovon, Sartaz Islam Hasnat, Md. Abrar Na s, Md. Fahim
format	Thesis
author	Mahmud, Shakib Izaz Shovon, Sartaz Islam Hasnat, Md. Abrar Na s, Md. Fahim
author_sort	Mahmud, Shakib Izaz
title	A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
title_short	A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
title_full	A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
title_fullStr	A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
title_full_unstemmed	A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
title_sort	color vision approach considering reflection co efficient based on autoencoder techniques using deep neural networks
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/17125
work_keys_str_mv	AT mahmudshakibizaz acolorvisionapproachconsideringreflectioncoefficientbasedonautoencodertechniquesusingdeepneuralnetworks AT shovonsartazislam acolorvisionapproachconsideringreflectioncoefficientbasedonautoencodertechniquesusingdeepneuralnetworks AT hasnatmdabrar acolorvisionapproachconsideringreflectioncoefficientbasedonautoencodertechniquesusingdeepneuralnetworks AT nasmdfahim acolorvisionapproachconsideringreflectioncoefficientbasedonautoencodertechniquesusingdeepneuralnetworks AT mahmudshakibizaz colorvisionapproachconsideringreflectioncoefficientbasedonautoencodertechniquesusingdeepneuralnetworks AT shovonsartazislam colorvisionapproachconsideringreflectioncoefficientbasedonautoencodertechniquesusingdeepneuralnetworks AT hasnatmdabrar colorvisionapproachconsideringreflectioncoefficientbasedonautoencodertechniquesusingdeepneuralnetworks AT nasmdfahim colorvisionapproachconsideringreflectioncoefficientbasedonautoencodertechniquesusingdeepneuralnetworks
_version_	1814309788933160960

A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks

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