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Development of Sealant Detection Methods using Auto-Finding Algorithm in TFT-LCD Glass Bonding Process : 자동 인식 알고리즘을 이용한 TFT-LCD 합착 공정에서의 Sealant 검출 방법 개발
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박희재 | - |
dc.contributor.author | 윤대건 | - |
dc.date.accessioned | 2017-07-14T03:39:51Z | - |
dc.date.available | 2019-04-18 | - |
dc.date.issued | 2016-02 | - |
dc.identifier.other | 000000132623 | - |
dc.identifier.uri | https://hdl.handle.net/10371/123870 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 2. 박희재. | - |
dc.description.abstract | Thin Film Transistor-Liquid Crystal Displays (TFT-LCDs) is most worldwide used device in display industry. In bonding process Color Filter (CF)-substrate and TFT-substrate, they are bonded by sealant which has various overlapped patterns. Measurement of sealant-width which provides evaluation of well-bonded condition is important.
Various overlapped patterns and substrate-glass make hard to detect sealant and measure sealant-width. In order to detect sealant, the proposed method automatically sets Area of Interest (AOI) for sealant-width measurement. The modified Otsu method is proposed to eliminate substrate-glass area except sealant area. Modified application of gamma correction is used to select threshold for extracting standard of sealant intensity. In these methods, sealant can be detected automatically and measured in pixel level. For the precise measurement, quartic Facet model is proposed to convert pixel intensity to sub-pixel intensity. The Discrete Orthogonal Polynomials (DOP) are used to verify quartic Facet model. The experimental result shows that new method has higher detection rate than conventional method and good repeatability of measurement than pixel level. | - |
dc.description.tableofcontents | 1. Introduction 7
1.1. Study background 7 1.2. Purpose of research 8 2. Theoretical Background 9 2.1. Otsu method 9 2.2. Gamma correction 11 2.3. Facet model 12 2.3.1 Discrete Orthogonal Polynomials 12 2.3.2 Two-dimensional DOP 13 3. Automatic Sealant Detection 14 3.1. Problem of setting AOI 14 3.2. Classify sealant area and glass area 16 3.2.1 Determining presence of glass area 16 3.2.2 Modified Otsu method for classify area 18 3.3. Setting sealant intensity by histogram 20 3.4. Setting AOI by deviation of average Y-profile 22 4. Sub-pixel Modeling of Sealant Image 23 4.1. Analysis of pixel intensity at edge 23 4.2. Sub-pixel modeling by quartic Facet model 24 5. Experiments and Evaluation 25 5.1. Evaluation of automatic sealant detection 25 5.2. Evaluation of new sub-pixel level measurement 28 6. Conclusion 30 Bibliography 31 Abstract in Korean 32 | - |
dc.format | application/pdf | - |
dc.format.extent | 2128115 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Sealant | - |
dc.subject | Otsu method | - |
dc.subject | Gamma correction | - |
dc.subject | Sub-pixel | - |
dc.subject | Quartic Facet model | - |
dc.subject | Discrete Orthogonal Polynomials | - |
dc.subject.ddc | 621 | - |
dc.title | Development of Sealant Detection Methods using Auto-Finding Algorithm in TFT-LCD Glass Bonding Process | - |
dc.title.alternative | 자동 인식 알고리즘을 이용한 TFT-LCD 합착 공정에서의 Sealant 검출 방법 개발 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Dae Geon Yoon | - |
dc.description.degree | Master | - |
dc.citation.pages | 33 | - |
dc.contributor.affiliation | 공과대학 기계항공공학부 | - |
dc.date.awarded | 2016-02 | - |
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