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Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad

Cyanobacteria can be used as indicators to offer relatively exclusive information pertaining to ecosystem condition. Cyanobacteria react quickly and predictably to a broad range of pollutant. Thus provides potentially constructive early caution signals of worsening environment and the possible ca...

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Main Author: Salem Saad, Awatef Saad
Format: Thesis
Published: 2012
Subjects:
QH301 Biology
Online Access:http://studentsrepo.um.edu.my/3768/1/1._Title_page%2C_abstract%2C_content.pdf
http://studentsrepo.um.edu.my/3768/2/2._Chapter_1_%E2%80%93_5.pdf
http://studentsrepo.um.edu.my/3768/3/3._References.pdf
http://studentsrepo.um.edu.my/3768/4/4._Appendices.pdf
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spelling my.um.stud.37682013-09-06T08:27:12Z Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad Salem Saad, Awatef Saad QH301 Biology Cyanobacteria can be used as indicators to offer relatively exclusive information pertaining to ecosystem condition. Cyanobacteria react quickly and predictably to a broad range of pollutant. Thus provides potentially constructive early caution signals of worsening environment and the possible causes. Therefore the aim of this study is to develop an image processing and pattern recognition methods to detect and classify oscillatoria genus from Cyanobacteria found on tropical Putrajaya Lake (Malaysia). Computer-based image analysis and pattern recognition methods were used to construct a system that is able to identify, and classify selected Cyanobacteria genus automatically. An image analysis algorithm was implemented to contrast, filter, isolate and recognize objects from microscope images. Image preprocessing module used to contrast images, to remove the noise, and to improve image quality. Segmentation module used to isolate the different objects found in input image. A combination of Feed forward artificial neural network (ANN) with feature extraction module was used to train and recognize oscillator images. System accuracy was measured by using manual and automated classifying methods, and developed system showed a great accuracy system reach to 90%. 2012 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/3768/1/1._Title_page%2C_abstract%2C_content.pdf application/pdf http://studentsrepo.um.edu.my/3768/2/2._Chapter_1_%E2%80%93_5.pdf application/pdf http://studentsrepo.um.edu.my/3768/3/3._References.pdf application/pdf http://studentsrepo.um.edu.my/3768/4/4._Appendices.pdf http://pendeta.um.edu.my/client/default/search/results?qu=Automatic+recognition+of+freshwater+algae+%28Oscillatoria+sp.%29+using+image+processing+techniques+with+artificial+neural+network+approach&te= Salem Saad, Awatef Saad (2012) Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad. Masters thesis, University of Malaya. http://studentsrepo.um.edu.my/3768/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic QH301 Biology
spellingShingle QH301 Biology
Salem Saad, Awatef Saad
Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad
description Cyanobacteria can be used as indicators to offer relatively exclusive information pertaining to ecosystem condition. Cyanobacteria react quickly and predictably to a broad range of pollutant. Thus provides potentially constructive early caution signals of worsening environment and the possible causes. Therefore the aim of this study is to develop an image processing and pattern recognition methods to detect and classify oscillatoria genus from Cyanobacteria found on tropical Putrajaya Lake (Malaysia). Computer-based image analysis and pattern recognition methods were used to construct a system that is able to identify, and classify selected Cyanobacteria genus automatically. An image analysis algorithm was implemented to contrast, filter, isolate and recognize objects from microscope images. Image preprocessing module used to contrast images, to remove the noise, and to improve image quality. Segmentation module used to isolate the different objects found in input image. A combination of Feed forward artificial neural network (ANN) with feature extraction module was used to train and recognize oscillator images. System accuracy was measured by using manual and automated classifying methods, and developed system showed a great accuracy system reach to 90%.
format Thesis
author Salem Saad, Awatef Saad
author_facet Salem Saad, Awatef Saad
author_sort Salem Saad, Awatef Saad
title Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad
title_short Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad
title_full Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad
title_fullStr Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad
title_full_unstemmed Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad
title_sort automatic recognition of freshwater algae (oscillatoria sp.) using image processing techniques with artificial neural network approach / awatef saad salem saad
publishDate 2012
url http://studentsrepo.um.edu.my/3768/1/1._Title_page%2C_abstract%2C_content.pdf
http://studentsrepo.um.edu.my/3768/2/2._Chapter_1_%E2%80%93_5.pdf
http://studentsrepo.um.edu.my/3768/3/3._References.pdf
http://studentsrepo.um.edu.my/3768/4/4._Appendices.pdf
http://pendeta.um.edu.my/client/default/search/results?qu=Automatic+recognition+of+freshwater+algae+%28Oscillatoria+sp.%29+using+image+processing+techniques+with+artificial+neural+network+approach&te=
http://studentsrepo.um.edu.my/3768/
_version_ 1738505604193845248
score 13.211869

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