dc.contributor.advisor | Alam, Md. Ashraful | |
dc.contributor.author | Mahbub, Mohammed Julfikar Ali | |
dc.contributor.author | Rahmatullah, S. Afsan | |
dc.contributor.author | Rahman, G M Sohanur | |
dc.contributor.author | Zillanee, Abu Hasnayen | |
dc.contributor.author | Akib, Aknur Kamal | |
dc.date.accessioned | 2022-06-01T09:28:11Z | |
dc.date.available | 2022-06-01T09:28:11Z | |
dc.date.copyright | 2022 | |
dc.date.issued | 2022-01 | |
dc.identifier.other | ID 21341031 | |
dc.identifier.other | ID 21241075 | |
dc.identifier.other | ID 21341049 | |
dc.identifier.other | ID 18301159 | |
dc.identifier.other | ID 18301209 | |
dc.identifier.uri | http://hdl.handle.net/10361/16818 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 23-26). | |
dc.description.abstract | Processing sketches to produce realistic images is an intriguing idea in the world
of emerging Artificial Intelligence. We present a Generative Adversarial Network
(GAN) based methodology that creates satisfactory images for the most prevalent
categories in our approach. The proposed approach is applicable not just to people,
but also to animals, objects and foods. The system takes a sketch and analyzes it
using a powerful neural engine to produce new photographs that resemble realistic
images. We also used a data augmentation method to dramatically increase the
variety of data available for training models. The proposed model has achieved
approximately 96.36% accuracy over generating sketch to realistic images of people
and 40.63% accuracy for objects and animals. Moreover, about 76.63% accuracy on
generating sketches from strokes on an average from people class. | en_US |
dc.description.statementofresponsibility | Mohammed Julfikar Ali Mahbub | |
dc.description.statementofresponsibility | S. Afsan Rahmatullah | |
dc.description.statementofresponsibility | G M Sohanur Rahman | |
dc.description.statementofresponsibility | Abu Hasnayen Zillanee | |
dc.description.statementofresponsibility | Aknur Kamal Akib | |
dc.format.extent | 26 pages | |
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 | GAN | en_US |
dc.subject | Sketch to realistic image | en_US |
dc.subject | Data augmentation | en_US |
dc.subject | Diversity of domain | en_US |
dc.subject.lcsh | Cognitive learning theory (Deep learning) | |
dc.subject.lcsh | Artificial intelligence | |
dc.title | Generation of realistic images from human drawn sketches using deep learning | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |