Primitives extraction for structural pattern recognition

This dissertation presents the investigations conducted on the various corner detection operators in terms of their working principles and detection algorithms. These operators are broadly classified under two categories: ones that require pre-processing of the images of the objects which are in bin...

Full description

Saved in:
Bibliographic Details
Main Author: Koo, Chee Keong.
Other Authors: Chan, Kap Luk
Format: Theses and Dissertations
Language:English
Published: 2008
Subjects:
Online Access:http://hdl.handle.net/10356/13165
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-13165
record_format dspace
spelling sg-ntu-dr.10356-131652023-07-04T15:46:07Z Primitives extraction for structural pattern recognition Koo, Chee Keong. Chan, Kap Luk School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing This dissertation presents the investigations conducted on the various corner detection operators in terms of their working principles and detection algorithms. These operators are broadly classified under two categories: ones that require pre-processing of the images of the objects which are in binary format and ones that are performed directly on the images of objects in grey level without requiring the additional pre-processing steps. In view of these, the pre-processing operations such as noise reductions, edge detection and edge thinning are investigated as well. This is to provide a reference point for the comparison on the effect of these pre-processing operations on the comer operators. The results are then compared with the corner operators that do not require these pre-processing. Images of objects with different sizes, texture, shapes and under various background conditions are used in the investigations. This is to provide a comparison platform in terms of the response of these comers operators towards different contrast, shape and noise. Master of Science (Computer Control and Automation) 2008-10-20T07:16:57Z 2008-10-20T07:16:57Z 1999 1999 Thesis http://hdl.handle.net/10356/13165 en 80 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::Electronic systems::Signal processing
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Koo, Chee Keong.
Primitives extraction for structural pattern recognition
description This dissertation presents the investigations conducted on the various corner detection operators in terms of their working principles and detection algorithms. These operators are broadly classified under two categories: ones that require pre-processing of the images of the objects which are in binary format and ones that are performed directly on the images of objects in grey level without requiring the additional pre-processing steps. In view of these, the pre-processing operations such as noise reductions, edge detection and edge thinning are investigated as well. This is to provide a reference point for the comparison on the effect of these pre-processing operations on the comer operators. The results are then compared with the corner operators that do not require these pre-processing. Images of objects with different sizes, texture, shapes and under various background conditions are used in the investigations. This is to provide a comparison platform in terms of the response of these comers operators towards different contrast, shape and noise.
author2 Chan, Kap Luk
author_facet Chan, Kap Luk
Koo, Chee Keong.
format Theses and Dissertations
author Koo, Chee Keong.
author_sort Koo, Chee Keong.
title Primitives extraction for structural pattern recognition
title_short Primitives extraction for structural pattern recognition
title_full Primitives extraction for structural pattern recognition
title_fullStr Primitives extraction for structural pattern recognition
title_full_unstemmed Primitives extraction for structural pattern recognition
title_sort primitives extraction for structural pattern recognition
publishDate 2008
url http://hdl.handle.net/10356/13165
_version_ 1772827583981813760