DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Chang-Ock | - |
dc.contributor.advisor | 이창옥 | - |
dc.contributor.author | Kim, Seongeun | - |
dc.date.accessioned | 2023-06-22T19:33:50Z | - |
dc.date.available | 2023-06-22T19:33:50Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1007824&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308566 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 수리과학과, 2022.8,[v, 41 p. :] | - |
dc.description.abstract | In human teeth images taken outside the oral cavity with a general digital camera, it is difficult to segment individual tooth due to common obstacles such as weak edges, intensity inhomogeneities and strong light reflections. In this work, we propose a method for segmenting individual tooth in human teeth images. The key to this method is to obtain pseudo edge-region using deep neural networks. We present a strategy for training neural networks while significantly simplifying the labeling process of training data using model-based methods. After an additional step to obtain initial contours for each tooth region, the individual tooth is segmented by applying active contour models. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Image segmentation▼aTooth segmentation▼aNeural network▼aGeometric attraction-driven flow▼aEdge-region▼aLight reflection | - |
dc.subject | 영상 분할▼a치아 분할▼a심층 신경망▼a기하적 유동▼a에지 영역▼a빛 반사 | - |
dc.title | Individual tooth segmentation in human teeth images using deep neural networks | - |
dc.title.alternative | 심층신경망을 이용한 사람 치아 영상에서의 개별 치아 분할 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :수리과학과, | - |
dc.contributor.alternativeauthor | 김성은 | - |
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