A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
| bracu.degree.level | Undergraduate | |
| bracu.type.group | Student Works | |
| datacite.rights | Open Access | |
| dc.contributor.advisor | Alam, Md. Ashraful | |
| dc.contributor.author | Mahmud, Shakib Izaz | |
| dc.contributor.author | Shovon, Sartaz Islam | |
| dc.contributor.author | Hasnat, Md. Abrar | |
| dc.contributor.author | Na s, Md. Fahim | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2022-08-28T08:38:47Z | |
| dc.date.available | 2022-08-28T08:38:47Z | |
| dc.date.copyright | 2021 | |
| dc.date.issued | 2021-09 | |
| dc.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 46-48). | |
| dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.degree | Bachelor of Science in Computer Science | |
| dc.description.statementofresponsibility | Shakib Izaz Mahmud | |
| dc.description.statementofresponsibility | Md. Abrar Hasnat | |
| dc.description.statementofresponsibility | Sartaz Islam Shovon | |
| dc.description.statementofresponsibility | Md. Fahim Na s | |
| dc.format.extent | 48 pages | |
| dc.identifier.other | ID 17101269 | |
| dc.identifier.other | ID 17101178 | |
| dc.identifier.other | ID 17101276 | |
| dc.identifier.other | ID 17101171 | |
| dc.identifier.uri | http://hdl.handle.net/10361/17125 | |
| dc.language.iso | en | en_US |
| dc.publisher | BRAC University | en_US |
| dc.rights | 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. | |
| dc.subject | Auto Encoded Techniques | en_US |
| dc.subject | Reflection Co efficient | en_US |
| dc.subject | Neural networks | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | KNN | en_US |
| dc.subject | ANN | en_US |
| dc.subject | Random forest | en_US |
| dc.subject | Logistic regression | en_US |
| dc.subject | Naive bayes | en_US |
| dc.subject | Decision tree | en_US |
| dc.subject | Digital Illuminance meter | en_US |
| dc.subject.lcsh | Cognitive learning theory (Deep learning) | |
| dc.subject.lcsh | Neural networks (Computer science) | |
| dc.title | A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks | en_US |
| dc.type | Thesis | en_US |