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...
| _version_ | 1848772490082385920 |
|---|---|
| author | Salem Saad, Awatef Saad |
| author_facet | Salem Saad, Awatef Saad |
| author_sort | Salem Saad, Awatef Saad |
| building | UM Research Repository |
| collection | Online Access |
| 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%. |
| first_indexed | 2025-11-14T13:27:20Z |
| format | Thesis |
| id | um-3768 |
| institution | University Malaya |
| institution_category | Local University |
| last_indexed | 2025-11-14T13:27:20Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | um-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/ |
| 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 |
| title | 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_short | 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 |
| topic | QH301 Biology |
| url | 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://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/3768/1/1._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 |