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.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
The development of multimedia technology leads to an increasing demand for diversified graphic color design. A fusion image reconstruction algorithm for fabric color extraction is proposed by combining fabric texture features. The study first extracts fabric features, then generates a matching color table, and reconstructs the image colors based on the color table. The proposed image color reconstruction algorithm is tested. After multiple iterations, the density peak clustering gradually converged to 0.36 on the test set. The density peak clustering algorithm had a high average pixel accuracy of 94.9% after multiple iterations, indicating a high pixel accuracy. The mean intersection over the union of the density peak clustering algorithm reached 91.1%, significantly higher than other methods. This proposed image reconstruction algorithm was compared with different methods. For the mean absolute error and mean square error, this method had the lowest values, which were 8.915 and 7.224, respectively. Its regression effect was good and the error was small. The proposed algorithm had the highest numerical value, with peak signal-to-noise ratio and structural similarity values of 0.875 and 28.733, respectively. It can accurately achieve image color reconstruction and then carry out planar color design.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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