Algoritma Pengekstrakan Imej Cap Jari Berdasarkan Analisa Variasi Arah
The goal of this research project was to produce an extraction algorithm capable of producing skeleton structure from a fingerprint image. As a result, an extraction algorithm capable of extracting a skeleton structure from a digital 256 gray level fingerprint image was developed. A technique to ide...
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my-uum-etd.7322013-07-24T12:08:45Z Algoritma Pengekstrakan Imej Cap Jari Berdasarkan Analisa Variasi Arah 2002 Nurzaid, Muhd. Zain Sekolah Siswazah Sekolah Siswazah HV Social Pathology. Social and Public Welfare The goal of this research project was to produce an extraction algorithm capable of producing skeleton structure from a fingerprint image. As a result, an extraction algorithm capable of extracting a skeleton structure from a digital 256 gray level fingerprint image was developed. A technique to identify the existence of ridge structure within a 16x16 block of image was designed. This technique uses four samples from four different directions to produce four waveform graphs that exhibit the characteristics of the image contained in a block. Several processing procedures was designed to perform various important tasks such as separating background from the foreground, detecting edges, thinning ridges, removing noises and converting into binary form. These tasks was performed mainly using a 3x3 matrics operation over an area of 3x3 pixels. The development process and problems encountered during the development phase his also being described. The proposed algorithm was tested and proved to achieve about 70% of success rate. 2002 Thesis https://etd.uum.edu.my/732/ https://etd.uum.edu.my/732/1/NURZAID_B._MUHD_ZAIN.pdf application/pdf eng validuser https://etd.uum.edu.my/732/2/1.NURZAID_B._MUHD_ZAIN.pdf application/pdf eng public masters masters Universiti Utara Malaysia |
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Universiti Utara Malaysia |
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eng eng |
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HV Social Pathology Social and Public Welfare |
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HV Social Pathology Social and Public Welfare Nurzaid, Muhd. Zain Algoritma Pengekstrakan Imej Cap Jari Berdasarkan Analisa Variasi Arah |
description |
The goal of this research project was to produce an extraction algorithm capable of producing skeleton structure from a fingerprint image. As a result, an extraction algorithm capable of extracting a skeleton structure from a digital 256 gray level fingerprint image was developed. A technique to identify the existence of ridge structure within a 16x16 block of image was designed. This technique uses four samples from four different directions to produce four waveform graphs that exhibit the characteristics of the image contained in a block. Several processing procedures was designed to perform various important tasks such as separating background from the foreground, detecting edges, thinning ridges, removing noises and converting into binary form. These tasks was performed mainly using a 3x3 matrics
operation over an area of 3x3 pixels. The development process and problems encountered during the development phase his also being described. The proposed algorithm was tested and proved to achieve about 70% of success rate. |
format |
Thesis |
qualification_name |
masters |
qualification_level |
Master's degree |
author |
Nurzaid, Muhd. Zain |
author_facet |
Nurzaid, Muhd. Zain |
author_sort |
Nurzaid, Muhd. Zain |
title |
Algoritma Pengekstrakan Imej Cap Jari Berdasarkan Analisa Variasi Arah
|
title_short |
Algoritma Pengekstrakan Imej Cap Jari Berdasarkan Analisa Variasi Arah
|
title_full |
Algoritma Pengekstrakan Imej Cap Jari Berdasarkan Analisa Variasi Arah
|
title_fullStr |
Algoritma Pengekstrakan Imej Cap Jari Berdasarkan Analisa Variasi Arah
|
title_full_unstemmed |
Algoritma Pengekstrakan Imej Cap Jari Berdasarkan Analisa Variasi Arah
|
title_sort |
algoritma pengekstrakan imej cap jari berdasarkan analisa variasi arah |
granting_institution |
Universiti Utara Malaysia |
granting_department |
Sekolah Siswazah |
publishDate |
2002 |
url |
https://etd.uum.edu.my/732/1/NURZAID_B._MUHD_ZAIN.pdf https://etd.uum.edu.my/732/2/1.NURZAID_B._MUHD_ZAIN.pdf |
_version_ |
1747826984826699776 |