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        <identifier>oai:nagoya.repo.nii.ac.jp:00019039</identifier>
        <datestamp>2023-01-16T04:08:00Z</datestamp>
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          <dc:title xml:lang="ja">各種信号処理・統計的手法の電子顕微鏡データへの応用</dc:title>
          <dcterms:alternative xml:lang="en">Application of signal processing and statistical processing to electron microscopic datasets</dcterms:alternative>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="ja">武藤, 俊介</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Muto, Shunsuke</jpcoar:creatorName>
          </jpcoar:creator>
          <dcterms:accessRights rdf:resource="http://purl.org/coar/access_right/c_abf2">open access</dcterms:accessRights>
          <dc:rights xml:lang="ja">著作権は公益社団法人日本顕微鏡学会が所有する</dc:rights>
          <jpcoar:subject subjectScheme="Other">ウェーブレット</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">PIXON法</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">多変量解析</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">電子エネルギー損失分光</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">スペクトラムイメージ</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">wavelet</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">PIXON method</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">multivariate analysis</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">electron energy-loss spectroscopy</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">spectrum image</jpcoar:subject>
          <datacite:description xml:lang="ja" descriptionType="Abstract">電子顕微鏡関連装置も近年デジタル化・自動化が進みつつある．この様な状況下で電子顕微鏡像及び関連分光データに内在する情報を最大限利用（特にノイズ除去，分解能向上，統計的情報抽出）するためには，データの特性に即した数学的な処理を通して情報抽出することが極めて有効である．本稿では筆者がこれまで行ってきた各種の信号・画像処理法について，その特徴と応用例を紹介する．</datacite:description>
          <datacite:description xml:lang="en" descriptionType="Abstract">Recent trends have pushed the scientific instruments associated with electron microscopy toward computer-controlled and automatic operation. It could be very effective under such circumstances to apply mathematical treatments suitable for the microscopic and spectroscopic data characteristics and extract information embedded thereby as much as possible, particularly for noise reduction, resolution improvement and statistical information extraction. In the present article several signal and image processing techniques, their features and application examples, which have been done by the present author are introduced.</datacite:description>
          <dc:publisher xml:lang="ja">公益社団法人日本顕微鏡学会</dc:publisher>
          <datacite:date dateType="Issued">2011</datacite:date>
          <dc:language>jpn</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_6501">journal article</dc:type>
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          <jpcoar:identifier identifierType="HDL">http://hdl.handle.net/2237/21127</jpcoar:identifier>
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          <jpcoar:sourceIdentifier identifierType="PISSN">1349-0958</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle xml:lang="ja">顕微鏡</jpcoar:sourceTitle>
          <jpcoar:volume>46</jpcoar:volume>
          <jpcoar:issue>1</jpcoar:issue>
          <jpcoar:pageStart>24</jpcoar:pageStart>
          <jpcoar:pageEnd>29</jpcoar:pageEnd>
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            <datacite:date dateType="Available">2018-02-21</datacite:date>
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