Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2023-08-21T07:35:46Z | - |
dc.date.available | 2023-08-21T07:35:46Z | - |
dc.date.issued | 2012-06 | - |
dc.identifier.citation | 2012년도 대한전자공학회 하계학술대회 논문집, v. 35, NO. 1, Page. 1225-1228 | - |
dc.identifier.uri | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE02275308 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/185573 | - |
dc.description.abstract | In target recognition field, Fern algorithm has been researched because of the high-recognition accuracy and the simple structure. Feature vector of Fern are constructed randomly. Consequently target recognition accuracy is depended on randomness. This paper proposes the efficient method to construct feature vector for Fern. Firstly, the proposed method calculates a correlation coefficient between feature vectors and then uses a correlation coefficient for measurement about the uniformity of distribution of feature vector. we use 2bit binary pattern to feature vector. We present through experiment result the relation between the uniformity of distribution of feature vector and target recognition accuracy. | - |
dc.description.sponsorship | 이 논문은 국방과학연구소의 지원을 받아 수행된 연구임 (계약번호 UD100033FD) | - |
dc.language | ko | - |
dc.publisher | 대한전자공학회 | - |
dc.title | Fern 알고리즘에서 효율적인 특징 정보 구성 방법 | - |
dc.title.alternative | An Efficient Method to Construct Feature Vector for Fern | - |
dc.type | Article | - |
dc.relation.no | 1 | - |
dc.relation.volume | 35 | - |
dc.relation.page | 1225-1228 | - |
dc.relation.journal | 2012년도 대한전자공학회 하계학술대회 논문집 | - |
dc.contributor.googleauthor | 정우진 | - |
dc.contributor.googleauthor | 박진욱 | - |
dc.contributor.googleauthor | 김소현 | - |
dc.contributor.googleauthor | 문영식 | - |
dc.sector.campus | E | - |
dc.sector.daehak | 소프트웨어융합대학 | - |
dc.sector.department | 소프트웨어학부 | - |
dc.identifier.pid | ysmoon | - |
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