Biological viruses images recognition using artificial intelligence classifier/
This work is only limited to five difficult viruses that cannot be distinguished such as Adenovirus, Astrovirus, Cowpox, Dengue and Ebola from the TEM virus database. The database of whole virus images in this work only consists of upfront images and does not deal with different poses. Within this w...
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| Format: | Thesis Software eBook |
| Language: | English |
| Published: |
Perlis, Malaysia
Faculty of Engineering Technology
2018
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| Summary: | This work is only limited to five difficult viruses that cannot be distinguished such as Adenovirus, Astrovirus, Cowpox, Dengue and Ebola from the TEM virus database. The database of whole virus images in this work only consists of upfront images and does not deal with different poses. Within this work, only common features such as PCA, LBP and GLCM are adopted. However, different parameters and coefficient of features under different noises are acquired. Salt and pepper noise is used instead of other noises because this type of noise always appear in digital images and often times appropriated as a benchmark for filter performance evaluations |
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| Physical Description: | 1 CD-ROM 12 cm |
| Bibliography: | Includes bibliographical references. |