인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
학술대회자료
Full-text
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
In electronics and semiconductor industries, defect classification plays an important role in identifying the cause of defect occurrence. To increase the performance of defect classification, unnecessary information such as background needs to be eliminated due to the possibility of influencing classification result.
In this paper, we propose an auto isolation method which does not require any reference image to crop the defect region only. This method uses similarity between sub-images and modified converging squares algorithm to extract the defect region. Similarity between sub-images is used to visualize the defect region even in multi-background defect image. Converging squares algorithm is specifically modified so that it may locate the defect region even in noisy similarity result. The method was able to correctly crop the defect area in 93.94% of mono-background defect image test set and 79.94% in multi-background defect image test set.
In this paper, we propose an auto isolation method which does not require any reference image to crop the defect region only. This method uses similarity between sub-images and modified converging squares algorithm to extract the defect region. Similarity between sub-images is used to visualize the defect region even in multi-background defect image. Converging squares algorithm is specifically modified so that it may locate the defect region even in noisy similarity result. The method was able to correctly crop the defect area in 93.94% of mono-background defect image test set and 79.94% in multi-background defect image test set.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2017-569-001939405
