Computer vision for advanced electron microscopy analysis
Transmission electron microscopy (TEM) is one of the most powerful techniques used to characterize materials. However, characterization of a set of heterogenous sample images still remains as a persistent challenge. This can be a painstaking process in which researches may spend a tremendous amount...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/77390 |
| _version_ | 1811690666506321920 |
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| author | Darmajaya, Devina |
| author2 | Li Shuzhou |
| author_facet | Li Shuzhou Darmajaya, Devina |
| author_sort | Darmajaya, Devina |
| collection | NTU |
| description | Transmission electron microscopy (TEM) is one of the most powerful techniques used to characterize materials. However, characterization of a set of heterogenous sample images still remains as a persistent challenge. This can be a painstaking process in which researches may spend a tremendous amount of time to classify particle’s crystal structures manually. Moreover, this may also lead to sampling bias by reporting the favourable image in the report that is not representative of the whole sample. Therefore, this study aims to provide solution to this problem by utilizing machine learning. The project focuses especially in building an optimized model to recognize synthetic images with convolutional neural network (CNN). Synthetic images were generated with MATLAB due to relevant dataset is not present at the moment. Afterwards, a CNN model was built and optimized by adjusting several parameters including choice of optimizer, loss function and regularization in Python. The model presented was able to attain 98.44% accuracy and 0.034 loss. This preliminary model is hopefully able to accelerate future works in recognizing synthetic images and real TEM images by transfer learning. |
| first_indexed | 2024-10-01T06:07:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/77390 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:07:37Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/773902023-03-04T15:43:25Z Computer vision for advanced electron microscopy analysis Darmajaya, Devina Li Shuzhou School of Materials Science and Engineering DRNTU::Engineering::Materials Transmission electron microscopy (TEM) is one of the most powerful techniques used to characterize materials. However, characterization of a set of heterogenous sample images still remains as a persistent challenge. This can be a painstaking process in which researches may spend a tremendous amount of time to classify particle’s crystal structures manually. Moreover, this may also lead to sampling bias by reporting the favourable image in the report that is not representative of the whole sample. Therefore, this study aims to provide solution to this problem by utilizing machine learning. The project focuses especially in building an optimized model to recognize synthetic images with convolutional neural network (CNN). Synthetic images were generated with MATLAB due to relevant dataset is not present at the moment. Afterwards, a CNN model was built and optimized by adjusting several parameters including choice of optimizer, loss function and regularization in Python. The model presented was able to attain 98.44% accuracy and 0.034 loss. This preliminary model is hopefully able to accelerate future works in recognizing synthetic images and real TEM images by transfer learning. Bachelor of Engineering (Materials Engineering) 2019-05-28T03:24:04Z 2019-05-28T03:24:04Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77390 en Nanyang Technological University 34 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Materials Darmajaya, Devina Computer vision for advanced electron microscopy analysis |
| title | Computer vision for advanced electron microscopy analysis |
| title_full | Computer vision for advanced electron microscopy analysis |
| title_fullStr | Computer vision for advanced electron microscopy analysis |
| title_full_unstemmed | Computer vision for advanced electron microscopy analysis |
| title_short | Computer vision for advanced electron microscopy analysis |
| title_sort | computer vision for advanced electron microscopy analysis |
| topic | DRNTU::Engineering::Materials |
| url | http://hdl.handle.net/10356/77390 |
| work_keys_str_mv | AT darmajayadevina computervisionforadvancedelectronmicroscopyanalysis |