Visual object recognition using deep convolutional neural network

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.

Détails bibliographiques
Auteur principal:	Ahmed, Sabbir
Autres auteurs:	Mostakim, Moin
Format:	Thèse
Langue:	English
Publié:	BRAC University 2017
Sujets:	Visual object Deep convolutional neural network Deep learning Convolutional neural network Object recognition Data augmentation
Accès en ligne:	http://hdl.handle.net/10361/8117

id	10361-8117
record_format	dspace
spelling	10361-81172022-01-26T10:13:22Z Visual object recognition using deep convolutional neural network Ahmed, Sabbir Mostakim, Moin Department of Computer Science and Engineering, BRAC University Visual object Deep convolutional neural network Deep learning Convolutional neural network Object recognition Data augmentation This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. Cataloged from PDF version of thesis report. Includes bibliographical references (page 30-31). Visual object recognition has been lying at the convergence point between machine learning, computer vision and AI since the very beginning. From robotics to information retrieval, many desired applications demand the ability to identify and localize objects into different categories. Despite a number of object recognition algorithms and systems being proposed for a long time in order to address this problem, there still lacks a general and comprehensive solution for the modern challenges. Most prominently, new approaches and computational models of vision to analyzing data, such as the convolutional neural networks (CNNs), have enabled a much more nuanced understanding of visual representation. In this paper, I have proposed a deep CNN model to solve the aforementioned problem of object recognition and reported a promising performance on a benchmark classification dataset called CIFAR10. Sabbir Ahmed B. Computer Science and Engineering 2017-05-11T06:50:01Z 2017-05-11T06:50:01Z 2017 2017 Thesis ID 17141013 http://hdl.handle.net/10361/8117 en BRAC University thesis 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. 31 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Visual object Deep convolutional neural network Deep learning Convolutional neural network Object recognition Data augmentation
spellingShingle	Visual object Deep convolutional neural network Deep learning Convolutional neural network Object recognition Data augmentation Ahmed, Sabbir Visual object recognition using deep convolutional neural network
description	This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
author2	Mostakim, Moin
author_facet	Mostakim, Moin Ahmed, Sabbir
format	Thesis
author	Ahmed, Sabbir
author_sort	Ahmed, Sabbir
title	Visual object recognition using deep convolutional neural network
title_short	Visual object recognition using deep convolutional neural network
title_full	Visual object recognition using deep convolutional neural network
title_fullStr	Visual object recognition using deep convolutional neural network
title_full_unstemmed	Visual object recognition using deep convolutional neural network
title_sort	visual object recognition using deep convolutional neural network
publisher	BRAC University
publishDate	2017
url	http://hdl.handle.net/10361/8117
work_keys_str_mv	AT ahmedsabbir visualobjectrecognitionusingdeepconvolutionalneuralnetwork
_version_	1814308301678051328

Visual object recognition using deep convolutional neural network

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