Harnessing object detection for learning

The current rapid development of technology and applications of object detection has always been an important Image recognition is a research area that is ongoing and is always challenging to task it in computer vision in many areas. There is a large array of different object categories, hence we ne...

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Main Author: Yap, Jinson
Other Authors: Wang Han
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/77715
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-777152023-07-07T16:06:55Z Harnessing object detection for learning Yap, Jinson Wang Han School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The current rapid development of technology and applications of object detection has always been an important Image recognition is a research area that is ongoing and is always challenging to task it in computer vision in many areas. There is a large array of different object categories, hence we need to train. Object recognition for new object in datasets requires more time to process to those classifiers, as it needs to be trained to allow the database to increase. However, there are existing file that have datasets like TensorFlow. This project proposed to use this content to implement on app to enhance the children’s education through technology. Education is key to development in kids learning ability and with this project it will enhance the kids learning. This project labels each individual elements of an image into its own category regions and provide a label for each object. The of methods extracting features from an annotated image are store into database containing about 100000 images and 200 objects. Every parameter has its own futures that can be explored, and analysed to achieved the best accuracy. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-04T05:56:11Z 2019-06-04T05:56:11Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77715 en Nanyang Technological University 52 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Yap, Jinson
Harnessing object detection for learning
description The current rapid development of technology and applications of object detection has always been an important Image recognition is a research area that is ongoing and is always challenging to task it in computer vision in many areas. There is a large array of different object categories, hence we need to train. Object recognition for new object in datasets requires more time to process to those classifiers, as it needs to be trained to allow the database to increase. However, there are existing file that have datasets like TensorFlow. This project proposed to use this content to implement on app to enhance the children’s education through technology. Education is key to development in kids learning ability and with this project it will enhance the kids learning. This project labels each individual elements of an image into its own category regions and provide a label for each object. The of methods extracting features from an annotated image are store into database containing about 100000 images and 200 objects. Every parameter has its own futures that can be explored, and analysed to achieved the best accuracy.
author2 Wang Han
author_facet Wang Han
Yap, Jinson
format Final Year Project
author Yap, Jinson
author_sort Yap, Jinson
title Harnessing object detection for learning
title_short Harnessing object detection for learning
title_full Harnessing object detection for learning
title_fullStr Harnessing object detection for learning
title_full_unstemmed Harnessing object detection for learning
title_sort harnessing object detection for learning
publishDate 2019
url http://hdl.handle.net/10356/77715
_version_ 1772827026128896000