碩士 / 國立中央大學 / 資訊工程學系 / 107 / Fish plays an important role in our life, but we can hardly to recognize the type of fish
without professional training. As a result, we would like to design a system which can segment
the part of fish from an image then classify the kind of each part of segments for ordinary
human being. Nevertheless, designing and implementing such system from scratch costs a lot
of human labor and time.
This dissertation proposes a fish image segmentation and classification system design
with the help of deep learning. We adopt the core concepts of MIAT Methodology to construct
the system with IDEF0 for modular and hierarchical system design and GRAFCET of discrete
event modeling. Also, we demonstrate the image annotation tool we use on labeling dataset
and state the protocols of image annotation. We adopt a fish image dataset to verify the system
created with applying MIAT Methodology within two months, and the system shows a top-1
accuracy of 85%.
| Identifer | oai:union.ndltd.org:TW/107NCU05392169 |
| Date | January 2019 |
| Creators | Lu-Hsuan Chen, 陳履軒 |
| Contributors | 陳慶瀚 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 76 |
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