Development of a real-time automated family classification system

The local binary pattern LBP, which was originally used for texture analysis, is now being proposed for improvement by the addition of threshold information. The LBP is superior in computational simplicity and this allows this project to be implemented in real time family classification system. As s...

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Main Author: Chen, George Fengrong.
Other Authors: Teoh Eam Khwang
Format: Final Year Project
Language:English
Published: 2011
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Online Access:http://hdl.handle.net/10356/45358
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-453582023-07-07T16:51:59Z Development of a real-time automated family classification system Chen, George Fengrong. Teoh Eam Khwang School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision The local binary pattern LBP, which was originally used for texture analysis, is now being proposed for improvement by the addition of threshold information. The LBP is superior in computational simplicity and this allows this project to be implemented in real time family classification system. As such, this project has two main components, one is to access the performance of proposed LBP with thresholds information which is in the back end stage, and the other is to implement this in real time application which is in the front end stage. Accessing the performance of the thresholded LBP, in terms of its error rate and training time was experimented in this project. Experimental results showed that the thresholded LBP, with thresholds from -69 to +69, step size of 5, has an improved error rate of about 4% and the most significant improvements was 8% in the family classification experiment of 53x63 pictures. Bachelor of Engineering 2011-06-13T02:55:18Z 2011-06-13T02:55:18Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/45358 en Nanyang Technological University 89 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::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Chen, George Fengrong.
Development of a real-time automated family classification system
description The local binary pattern LBP, which was originally used for texture analysis, is now being proposed for improvement by the addition of threshold information. The LBP is superior in computational simplicity and this allows this project to be implemented in real time family classification system. As such, this project has two main components, one is to access the performance of proposed LBP with thresholds information which is in the back end stage, and the other is to implement this in real time application which is in the front end stage. Accessing the performance of the thresholded LBP, in terms of its error rate and training time was experimented in this project. Experimental results showed that the thresholded LBP, with thresholds from -69 to +69, step size of 5, has an improved error rate of about 4% and the most significant improvements was 8% in the family classification experiment of 53x63 pictures.
author2 Teoh Eam Khwang
author_facet Teoh Eam Khwang
Chen, George Fengrong.
format Final Year Project
author Chen, George Fengrong.
author_sort Chen, George Fengrong.
title Development of a real-time automated family classification system
title_short Development of a real-time automated family classification system
title_full Development of a real-time automated family classification system
title_fullStr Development of a real-time automated family classification system
title_full_unstemmed Development of a real-time automated family classification system
title_sort development of a real-time automated family classification system
publishDate 2011
url http://hdl.handle.net/10356/45358
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