메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술대회자료
저자정보
Hyungtae Kim (KITECH) Namkyeong Kim (Deagu Catholic University) Kyeongnan Nam (Deagu Catholic University) Kwon-Hee Nam (Kyungpook National University)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2018
발행연도
2018.10
수록면
756 - 761 (6page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
Machine vision has been applied to many industrial areas. It has also become popular in heritage science. This study proposes maximum contrast imaging to determine the appropriate light color and intensity to increase the discriminating ability for image analyses. The contrast of an image was evaluated in accordance to various digital focus indices (DFIs), and their formulations were thus investigated. Optimal conditions of focus, light color, and intensity were determined based on the maxima of the DFIs. Fuzzy logic was applied to verify the determined owing to the different DFI maxima. The proposed method was implemented subject to the assumption of an arbitrary number of light colors. In the experiment, the DFIs were constructed using C/C++ libraries compatible with OpenCV and the decision methods were verified using an automated optical inspection (AOI) machine. Test targets were old handwritings in a xylograph book, and optimal conditions were determined in the case of a multi-color light source. The handwritings were initially illegible but became readable using maximum contrast images. This study showed that maximum contrast imaging will be useful in industrial applications, such as automatic optical inspection, and in the analyses of historical objects.

목차

Abstract
1. INTRODUCTION
2. BASIC PRINCIPLES
3. EXPERIMENTS
4. RESULTS
5. CONCLUSION
6. ACKNOWLEDGEMENT
REFERENCES

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2018-003-003539024