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dc.contributor.advisorAlam, Md. Ashraful
dc.contributor.authorMahmud, Shakib Izaz
dc.contributor.authorShovon, Sartaz Islam
dc.contributor.authorHasnat, Md. Abrar
dc.contributor.authorNa s, Md. Fahim
dc.date.accessioned2022-08-28T08:38:47Z
dc.date.available2022-08-28T08:38:47Z
dc.date.copyright2021
dc.date.issued2021-09
dc.identifier.otherID 17101269
dc.identifier.otherID 17101178
dc.identifier.otherID 17101276
dc.identifier.otherID 17101171
dc.identifier.urihttp://hdl.handle.net/10361/17125
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 46-48).
dc.description.abstractColor 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.en_US
dc.description.statementofresponsibilityShakib Izaz Mahmud
dc.description.statementofresponsibilityMd. Abrar Hasnat
dc.description.statementofresponsibilitySartaz Islam Shovon
dc.description.statementofresponsibilityMd. Fahim Na s
dc.format.extent48 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac 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.
dc.subjectAuto Encoded Techniquesen_US
dc.subjectReflection Co efficienten_US
dc.subjectNeural networksen_US
dc.subjectDeep learningen_US
dc.subjectKNNen_US
dc.subjectANNen_US
dc.subjectRandom foresten_US
dc.subjectLogistic regressionen_US
dc.subjectNaive bayesen_US
dc.subjectDecision treeen_US
dc.subjectDigital Illuminance meteren_US
dc.subject.lcshCognitive learning theory (Deep learning)
dc.subject.lcshNeural networks (Computer science)
dc.titleA color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networksen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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