\documentclass[kind=ENG,a4j,bachelor,landscape,viewer,color,check]{edb-syl}
\def\年度{2011}
\def\和年度{23}
\begin{document}

\Englishfalse
%%%% 215473:215735
\begin{edb:lecture}%
\EdbEID{215735}\EdbEOID{558045}%
\JabeeEfalse%
\JabeeRfalse%
\包含false%
\授業ID{215473:215735}%
\授業概要種別{166226}%
\名称{画像処理工学}{Image Processing}%
\担当教員{\所属肩書氏名{知能情報工学科 知能工学講座}{講師}{\href{http://pub2.db.tokushima-u.ac.jp/ERD/person/82302/profile-ja.html}{\EdbSMG{カルンガル}{ギディンシ}{ステファン}}}}%
\開講学期{3年(後期)}%
\履修種別{選択}%
\EdbColDef{edb:lecture.goal}{1}{E}{Understanding indispensable basic image processing methods for visual recognition}%
\EdbColDef{edb:lecture.goal}{1}{J}{視覚情報処理技術に不可欠な画像処理技術の基本的な手法を理解し，応用力をつける．}%
\EdbColDef{edb:lecture.plan}{1}{E}{Features of digital image processing and image data structures}%
\EdbColDef{edb:lecture.plan}{1}{J}{ディジタル画像処理の特徴，画像データの取り扱い}%
\EdbColDef{edb:lecture.plan}{2}{E}{Histogram}%
\EdbColDef{edb:lecture.plan}{2}{J}{ヒストグラム，画像処理アルゴリズムの形態，画像の表現，データ構造}%
\EdbColDef{edb:lecture.plan}{3}{E}{Image formation, binary images, distance}%
\EdbColDef{edb:lecture.plan}{3}{J}{画像の2 値化，2 値画像の連結性と距離}%
\EdbColDef{edb:lecture.plan}{4}{E}{Transformation operations, connected elements, shape features}%
\EdbColDef{edb:lecture.plan}{4}{J}{連結成分の変形操作，図形の形状特徴}%
\EdbColDef{edb:lecture.plan}{5}{E}{Image conversion and emphasis}%
\EdbColDef{edb:lecture.plan}{5}{J}{画像の変換と強調}%
\EdbColDef{edb:lecture.plan}{6}{E}{Smoothing and noise elimination}%
\EdbColDef{edb:lecture.plan}{6}{J}{平滑化と雑音除去}%
\EdbColDef{edb:lecture.plan}{7}{E}{Restoration, re-composition and geometrical conversion}%
\EdbColDef{edb:lecture.plan}{7}{J}{画像の復元，画像の再構成，幾何学的変換}%
\EdbColDef{edb:lecture.plan}{8}{E}{Edge and line detection}%
\EdbColDef{edb:lecture.plan}{8}{J}{エッジ検出，線の検出}%
\EdbColDef{edb:lecture.plan}{9}{E}{Domain decomposition and texture analysis}%
\EdbColDef{edb:lecture.plan}{9}{J}{領域分割，テクスチャ解析}%
\EdbColDef{edb:lecture.plan}{10}{E}{3D image processing and motion sequence analysis}%
\EdbColDef{edb:lecture.plan}{10}{J}{マルチスペクトル画像処理，3 次元画像処理，動画像解析}%
\EdbColDef{edb:lecture.plan}{11}{E}{Pattern matching and classification}%
\EdbColDef{edb:lecture.plan}{11}{J}{パターンマッチング，教師付き分類，教師なし分類}%
\EdbColDef{edb:lecture.plan}{12}{E}{Image processing system}%
\EdbColDef{edb:lecture.plan}{12}{J}{画像処理システム}%
\EdbColDef{edb:lecture.plan}{13}{E}{Industrial image processing and shape recognition}%
\EdbColDef{edb:lecture.plan}{13}{J}{工業用画像処理1(工業用画像処理の要点，位置，形状の認識)}%
\EdbColDef{edb:lecture.plan}{14}{E}{Defects and surface information recognition}%
\EdbColDef{edb:lecture.plan}{14}{J}{工業用画像処理2(欠陥の認識，表面情報の認識)}%
\EdbColDef{edb:lecture.plan}{15}{E}{Questions and summary}%
\EdbColDef{edb:lecture.plan}{15}{J}{予備日}%
\EdbColDef{edb:lecture.plan}{16}{E}{Exam}%
\EdbColDef{edb:lecture.plan}{16}{J}{定期試験}%
\EdbColDef{edb:lecture.unit}{1}{E}{2}%
\EdbColDef{edb:lecture.unit}{1}{J}{2}%
\EdbColDef{edb:lecture.target}{1}{E}{Understanding indispensable basic image processing methods for visual recognition.}%
\EdbColDef{edb:lecture.target}{1}{J}{線形システム解析，信号処理工学及び演習，マイクロプロセッサを履修しておくことが望ましい．}%
\EdbColDef{edb:lecture.outline}{1}{E}{Image processing engineering is an entrance level lecture to master the basics of digital image processing techniques in medical imaging, industrial images, vision pattern processing, etc.\ The lectures includes the fundamental concepts of binary image processing, image conversion, emphasis, restoration, feature abstraction, pattern matching, classification, application of image processing systems and industrial image processing.\ Computer laboratory exercises will be conducted quarterly.\ When possible, a lecture on the most recent treads in industrial image processing by a specialist in the field could also be included.}%
\EdbColDef{edb:lecture.outline}{1}{J}{画像処理工学は，医療画像処理，工業用画像処理，視覚パターン処理等の基礎になるディジタル画像処理手法の基本的事項を修得するための講義であり，画像処理の基本概念，2値画像処理，画像の変換と強調，画像の復元，画像の特徴抽出，パターンマッチング，パターン分類，画像処理システム，および工業用画像処理への応用について講述する.\ なお，工業用画像処理については，その分野の専門家に集中講義を依頼することにより，広い最新情報を修得できるようにしている.}%
\EdbColDef{edb:lecture.keyword}{1}{E}{image processing}%
\EdbColDef{edb:lecture.keyword}{1}{J}{画像処理}%
\EdbColDef{edb:lecture.keyword}{2}{E}{pattern recognition}%
\EdbColDef{edb:lecture.keyword}{2}{J}{パターン認識}%
\EdbColDef{edb:lecture.foundation}{1}{@}{216090}%
\EdbColDef{edb:lecture.foundation}{1}{E}{\EdbSeeLecture{Linear System Analysis}{215472}{216090}{1.0}}%
\EdbColDef{edb:lecture.foundation}{1}{J}{\EdbSeeLecture{線形システム解析}{215472}{216090}{1.0}}%
\EdbColDef{edb:lecture.foundation}{2}{@}{216008}%
\EdbColDef{edb:lecture.foundation}{2}{E}{\EdbSeeLecture{Signal Processing}{215473}{216008}{1.0}}%
\EdbColDef{edb:lecture.foundation}{2}{J}{\EdbSeeLecture{信号処理工学}{215473}{216008}{1.0}}%
\EdbColDef{edb:lecture.foundation}{3}{@}{216416}%
\EdbColDef{edb:lecture.foundation}{3}{E}{\EdbSeeLecture{Microprocessors}{215473}{216416}{1.0}}%
\EdbColDef{edb:lecture.foundation}{3}{J}{\EdbSeeLecture{マイクロプロセッサ}{215473}{216416}{1.0}}%
\EdbColDef{edb:lecture.relation}{1}{@}{216008}%
\EdbColDef{edb:lecture.relation}{1}{E}{\EdbSeeLecture{Signal Processing}{215473}{216008}{0.5}}%
\EdbColDef{edb:lecture.relation}{1}{J}{\EdbSeeLecture{信号処理工学}{215473}{216008}{0.5}}%
\EdbColDef{edb:lecture.requirement}{1}{E}{``Linear system analysis'' (1.0), ``Signal processing'' (1.0), ``Microprocessors'' (1.0)}%
\EdbColDef{edb:lecture.requirement}{1}{J}{線形システム解析，信号処理工学及び演習，マイクロプロセッサを履修しておくことが望ましい}%
\EdbColDef{edb:lecture.notice}{1}{E}{none}%
\EdbColDef{edb:lecture.notice}{1}{J}{なし}%
\EdbColDef{edb:lecture.evaluation}{1}{E}{Result:{\EdbZSp} attendance (10\%) and reports,{\EdbZSp} programming exercise and continuous assessment tests (50\%),{\EdbZSp} final exam (40\%).}%
\EdbColDef{edb:lecture.evaluation}{1}{J}{出席 (10\%)，れぽーと，演習，小テスト(50\%),{\EdbZSp} 期末試験 (40\%).}%
\EdbColDef{edb:lecture.student}{1}{E}{Can be taken by only students in the night course}%
\EdbColDef{edb:lecture.student}{1}{J}{開講コースは夜間主コース学生のみ履修可能}%
\EdbColDef{edb:lecture.textbook}{1}{E}{Hideyuki Tamura "Introduction to computer image processing"}%
\EdbColDef{edb:lecture.textbook}{1}{J}{田村秀行編著 「コンピュータ画像処理入門」}%
\EdbColDef{edb:lecture.reference}{1}{E}{Mikio Takagi and Shimoda Yohisashi "Image analysis handbook"}%
\EdbColDef{edb:lecture.reference}{1}{J}{高木幹雄$\cdot{}$下田陽久 監修「画像解析ハンドブック」東京大学出版会}%
\EdbColDef{edb:lecture.note}{1}{E}{\_ To pass this class and to fully understand each lecture,{\EdbZSp} two hours each for preparation and review are necessary.}%
\EdbColDef{edb:lecture.note}{1}{J}{授業を受ける際には，2時間の授業時間毎に2時間の予習と2時間の復習をし たうえで授業を受けることが，授業の理解と単位取得のために必要である}%
\EdbColDef{edb:lecture.note}{2}{E}{This course will be evaluated as indicated in the evaluation criteria above The final exam will cover the whole course}%
\EdbColDef{edb:lecture.note}{2}{J}{授業を受ける際には，2 時間の授業時間毎に2 時間の予習と2 時間の復 習をしたうえで授業を受けることが，授業の理解と単位取得のために必要で ある}%
\EdbColDef{edb:lecture.contact}{1}{J}{\連絡先個人{}{カ{\EdbZSp}ル{\EdbZSp}ン{\EdbZSp}ガ{\EdbZSp}ル{\EdbZSp}({\EdbZSp}D{\EdbZSp}r{\EdbZSp}.{\EdbZSp}8{\EdbZSp}0{\EdbZSp}1{\EdbZSp},{\EdbZSp} {\EdbZSp}0{\EdbZSp}8{\EdbZSp}8{\EdbZSp}-{\EdbZSp}6{\EdbZSp}5{\EdbZSp}6{\EdbZSp}-{\EdbZSp}7{\EdbZSp}4{\EdbZSp}8{\EdbZSp}8{\EdbZSp},{\EdbZSp} {\EdbZSp}k{\EdbZSp}a{\EdbZSp}r{\EdbZSp}u{\EdbZSp}n{\EdbZSp}g{\EdbZSp}a{\EdbZSp}@{\EdbZSp}i{\EdbZSp}s{\EdbZSp}.{\EdbZSp}t{\EdbZSp}o{\EdbZSp}k{\EdbZSp}u{\EdbZSp}s{\EdbZSp}h{\EdbZSp}i{\EdbZSp}m{\EdbZSp}a{\EdbZSp}-{\EdbZSp}u{\EdbZSp}.{\EdbZSp}a{\EdbZSp}c{\EdbZSp}.{\EdbZSp}j{\EdbZSp}p{\EdbZSp}){\EdbZSp} {\EdbZSp}M{\EdbZSp}a{\EdbZSp}i{\EdbZSp}l{\EdbZSp} {\EdbZSp}({\EdbZSp}オ{\EdbZSp}フ{\EdbZSp}ィ{\EdbZSp}ス{\EdbZSp} {\EdbZSp}ア{\EdbZSp}ワ{\EdbZSp}ー{\EdbZSp}:{\EdbZSp} {\EdbZSp}火{\EdbZSp}曜{\EdbZSp}日{\EdbZSp}1{\EdbZSp}5{\EdbZSp}:{\EdbZSp}0{\EdbZSp}0{\EdbZSp}$\sim{}$1{\EdbZSp}7{\EdbZSp}:{\EdbZSp}0{\EdbZSp}0{\EdbZSp}({\EdbZSp}年{\EdbZSp}度{\EdbZSp}ご{\EdbZSp}と{\EdbZSp}に{\EdbZSp}学{\EdbZSp}科{\EdbZSp}の{\EdbZSp}掲{\EdbZSp}示{\EdbZSp}を{\EdbZSp}参{\EdbZSp}照{\EdbZSp}す{\EdbZSp}る{\EdbZSp}こ{\EdbZSp}と{\EdbZSp}){\EdbZSp}){\EdbZSp}}{}}%
\end{edb:lecture}%

\Englishtrue
%%%% 215473:215735
\begin{edb:lecture}%
\EdbEID{215735}\EdbEOID{558045}%
\JabeeEfalse%
\JabeeRfalse%
\包含false%
\授業ID{215473:215735}%
\授業概要種別{166226}%
\名称{Image Processing}{画像処理工学}%
\担当教員{\所属肩書氏名{Intelligent Systems, Department of Information Science and Intelligent Systems}{Associate Professor}{\href{http://pub2.db.tokushima-u.ac.jp/ERD/person/82302/profile-en.html}{\EdbGMS{Stephen}{Githinji}{Karungaru}}}}%
\開講学期{3rd-year(2nd semester)}%
\履修種別{selection}%
\EdbColDef{edb:lecture.goal}{1}{J}{視覚情報処理技術に不可欠な画像処理技術の基本的な手法を理解し，応用力をつける．}%
\EdbColDef{edb:lecture.goal}{1}{E}{Understanding indispensable basic image processing methods for visual recognition}%
\EdbColDef{edb:lecture.plan}{1}{J}{ディジタル画像処理の特徴，画像データの取り扱い}%
\EdbColDef{edb:lecture.plan}{1}{E}{Features of digital image processing and image data structures}%
\EdbColDef{edb:lecture.plan}{2}{J}{ヒストグラム，画像処理アルゴリズムの形態，画像の表現，データ構造}%
\EdbColDef{edb:lecture.plan}{2}{E}{Histogram}%
\EdbColDef{edb:lecture.plan}{3}{J}{画像の2 値化，2 値画像の連結性と距離}%
\EdbColDef{edb:lecture.plan}{3}{E}{Image formation, binary images, distance}%
\EdbColDef{edb:lecture.plan}{4}{J}{連結成分の変形操作，図形の形状特徴}%
\EdbColDef{edb:lecture.plan}{4}{E}{Transformation operations, connected elements, shape features}%
\EdbColDef{edb:lecture.plan}{5}{J}{画像の変換と強調}%
\EdbColDef{edb:lecture.plan}{5}{E}{Image conversion and emphasis}%
\EdbColDef{edb:lecture.plan}{6}{J}{平滑化と雑音除去}%
\EdbColDef{edb:lecture.plan}{6}{E}{Smoothing and noise elimination}%
\EdbColDef{edb:lecture.plan}{7}{J}{画像の復元，画像の再構成，幾何学的変換}%
\EdbColDef{edb:lecture.plan}{7}{E}{Restoration, re-composition and geometrical conversion}%
\EdbColDef{edb:lecture.plan}{8}{J}{エッジ検出，線の検出}%
\EdbColDef{edb:lecture.plan}{8}{E}{Edge and line detection}%
\EdbColDef{edb:lecture.plan}{9}{J}{領域分割，テクスチャ解析}%
\EdbColDef{edb:lecture.plan}{9}{E}{Domain decomposition and texture analysis}%
\EdbColDef{edb:lecture.plan}{10}{J}{マルチスペクトル画像処理，3 次元画像処理，動画像解析}%
\EdbColDef{edb:lecture.plan}{10}{E}{3D image processing and motion sequence analysis}%
\EdbColDef{edb:lecture.plan}{11}{J}{パターンマッチング，教師付き分類，教師なし分類}%
\EdbColDef{edb:lecture.plan}{11}{E}{Pattern matching and classification}%
\EdbColDef{edb:lecture.plan}{12}{J}{画像処理システム}%
\EdbColDef{edb:lecture.plan}{12}{E}{Image processing system}%
\EdbColDef{edb:lecture.plan}{13}{J}{工業用画像処理1(工業用画像処理の要点，位置，形状の認識)}%
\EdbColDef{edb:lecture.plan}{13}{E}{Industrial image processing and shape recognition}%
\EdbColDef{edb:lecture.plan}{14}{J}{工業用画像処理2(欠陥の認識，表面情報の認識)}%
\EdbColDef{edb:lecture.plan}{14}{E}{Defects and surface information recognition}%
\EdbColDef{edb:lecture.plan}{15}{J}{予備日}%
\EdbColDef{edb:lecture.plan}{15}{E}{Questions and summary}%
\EdbColDef{edb:lecture.plan}{16}{J}{定期試験}%
\EdbColDef{edb:lecture.plan}{16}{E}{Exam}%
\EdbColDef{edb:lecture.unit}{1}{J}{2}%
\EdbColDef{edb:lecture.unit}{1}{E}{2}%
\EdbColDef{edb:lecture.target}{1}{J}{線形システム解析，信号処理工学及び演習，マイクロプロセッサを履修しておくことが望ましい．}%
\EdbColDef{edb:lecture.target}{1}{E}{Understanding indispensable basic image processing methods for visual recognition.}%
\EdbColDef{edb:lecture.outline}{1}{J}{画像処理工学は，医療画像処理，工業用画像処理，視覚パターン処理等の基礎になるディジタル画像処理手法の基本的事項を修得するための講義であり，画像処理の基本概念，2値画像処理，画像の変換と強調，画像の復元，画像の特徴抽出，パターンマッチング，パターン分類，画像処理システム，および工業用画像処理への応用について講述する.\ なお，工業用画像処理については，その分野の専門家に集中講義を依頼することにより，広い最新情報を修得できるようにしている.}%
\EdbColDef{edb:lecture.outline}{1}{E}{Image processing engineering is an entrance level lecture to master the basics of digital image processing techniques in medical imaging, industrial images, vision pattern processing, etc.\ The lectures includes the fundamental concepts of binary image processing, image conversion, emphasis, restoration, feature abstraction, pattern matching, classification, application of image processing systems and industrial image processing.\ Computer laboratory exercises will be conducted quarterly.\ When possible, a lecture on the most recent treads in industrial image processing by a specialist in the field could also be included.}%
\EdbColDef{edb:lecture.keyword}{1}{J}{画像処理}%
\EdbColDef{edb:lecture.keyword}{1}{E}{image processing}%
\EdbColDef{edb:lecture.keyword}{2}{J}{パターン認識}%
\EdbColDef{edb:lecture.keyword}{2}{E}{pattern recognition}%
\EdbColDef{edb:lecture.foundation}{1}{@}{216090}%
\EdbColDef{edb:lecture.foundation}{1}{J}{\EdbSeeLecture{線形システム解析}{215472}{216090}{1.0}}%
\EdbColDef{edb:lecture.foundation}{1}{E}{\EdbSeeLecture{Linear System Analysis}{215472}{216090}{1.0}}%
\EdbColDef{edb:lecture.foundation}{2}{@}{216008}%
\EdbColDef{edb:lecture.foundation}{2}{J}{\EdbSeeLecture{信号処理工学}{215473}{216008}{1.0}}%
\EdbColDef{edb:lecture.foundation}{2}{E}{\EdbSeeLecture{Signal Processing}{215473}{216008}{1.0}}%
\EdbColDef{edb:lecture.foundation}{3}{@}{216416}%
\EdbColDef{edb:lecture.foundation}{3}{J}{\EdbSeeLecture{マイクロプロセッサ}{215473}{216416}{1.0}}%
\EdbColDef{edb:lecture.foundation}{3}{E}{\EdbSeeLecture{Microprocessors}{215473}{216416}{1.0}}%
\EdbColDef{edb:lecture.relation}{1}{@}{216008}%
\EdbColDef{edb:lecture.relation}{1}{J}{\EdbSeeLecture{信号処理工学}{215473}{216008}{0.5}}%
\EdbColDef{edb:lecture.relation}{1}{E}{\EdbSeeLecture{Signal Processing}{215473}{216008}{0.5}}%
\EdbColDef{edb:lecture.requirement}{1}{J}{線形システム解析，信号処理工学及び演習，マイクロプロセッサを履修しておくことが望ましい}%
\EdbColDef{edb:lecture.requirement}{1}{E}{``Linear system analysis'' (1.0), ``Signal processing'' (1.0), ``Microprocessors'' (1.0)}%
\EdbColDef{edb:lecture.notice}{1}{J}{なし}%
\EdbColDef{edb:lecture.notice}{1}{E}{none}%
\EdbColDef{edb:lecture.evaluation}{1}{J}{出席 (10\%)，れぽーと，演習，小テスト(50\%),{\EdbZSp} 期末試験 (40\%).}%
\EdbColDef{edb:lecture.evaluation}{1}{E}{Result:{\EdbZSp} attendance (10\%) and reports,{\EdbZSp} programming exercise and continuous assessment tests (50\%),{\EdbZSp} final exam (40\%).}%
\EdbColDef{edb:lecture.student}{1}{J}{開講コースは夜間主コース学生のみ履修可能}%
\EdbColDef{edb:lecture.student}{1}{E}{Can be taken by only students in the night course}%
\EdbColDef{edb:lecture.textbook}{1}{J}{田村秀行編著 「コンピュータ画像処理入門」}%
\EdbColDef{edb:lecture.textbook}{1}{E}{Hideyuki Tamura "Introduction to computer image processing"}%
\EdbColDef{edb:lecture.reference}{1}{J}{高木幹雄$\cdot{}$下田陽久 監修「画像解析ハンドブック」東京大学出版会}%
\EdbColDef{edb:lecture.reference}{1}{E}{Mikio Takagi and Shimoda Yohisashi "Image analysis handbook"}%
\EdbColDef{edb:lecture.note}{1}{J}{授業を受ける際には，2時間の授業時間毎に2時間の予習と2時間の復習をし たうえで授業を受けることが，授業の理解と単位取得のために必要である}%
\EdbColDef{edb:lecture.note}{1}{E}{\_ To pass this class and to fully understand each lecture,{\EdbZSp} two hours each for preparation and review are necessary.}%
\EdbColDef{edb:lecture.note}{2}{J}{授業を受ける際には，2 時間の授業時間毎に2 時間の予習と2 時間の復 習をしたうえで授業を受けることが，授業の理解と単位取得のために必要で ある}%
\EdbColDef{edb:lecture.note}{2}{E}{This course will be evaluated as indicated in the evaluation criteria above The final exam will cover the whole course}%
\EdbColDef{edb:lecture.contact}{1}{E}{\連絡先個人{}{({\EdbZSp}D{\EdbZSp}r{\EdbZSp}.{\EdbZSp}8{\EdbZSp}0{\EdbZSp}1{\EdbZSp},{\EdbZSp} {\EdbZSp}+{\EdbZSp}8{\EdbZSp}1{\EdbZSp}-{\EdbZSp}8{\EdbZSp}8{\EdbZSp}-{\EdbZSp}6{\EdbZSp}5{\EdbZSp}6{\EdbZSp}-{\EdbZSp}7{\EdbZSp}4{\EdbZSp}8{\EdbZSp}8{\EdbZSp},{\EdbZSp} {\EdbZSp}k{\EdbZSp}a{\EdbZSp}r{\EdbZSp}u{\EdbZSp}n{\EdbZSp}g{\EdbZSp}a{\EdbZSp}@{\EdbZSp}i{\EdbZSp}s{\EdbZSp}.{\EdbZSp}t{\EdbZSp}o{\EdbZSp}k{\EdbZSp}u{\EdbZSp}s{\EdbZSp}h{\EdbZSp}i{\EdbZSp}m{\EdbZSp}a{\EdbZSp}-{\EdbZSp}u{\EdbZSp}.{\EdbZSp}a{\EdbZSp}c{\EdbZSp}.{\EdbZSp}j{\EdbZSp}p{\EdbZSp}){\EdbZSp} {\EdbZSp}M{\EdbZSp}a{\EdbZSp}i{\EdbZSp}l{\EdbZSp} {\EdbZSp}({\EdbZSp}O{\EdbZSp}f{\EdbZSp}f{\EdbZSp}i{\EdbZSp}c{\EdbZSp}e{\EdbZSp} {\EdbZSp}H{\EdbZSp}o{\EdbZSp}u{\EdbZSp}r{\EdbZSp}:{\EdbZSp} {\EdbZSp}T{\EdbZSp}u{\EdbZSp}e{\EdbZSp}s{\EdbZSp}d{\EdbZSp}a{\EdbZSp}y{\EdbZSp} {\EdbZSp}s{\EdbZSp},{\EdbZSp} {\EdbZSp}1{\EdbZSp}5{\EdbZSp}:{\EdbZSp}0{\EdbZSp}0{\EdbZSp}\_{\EdbZSp} {\EdbZSp}1{\EdbZSp}7{\EdbZSp}:{\EdbZSp}0{\EdbZSp}0{\EdbZSp}){\EdbZSp}){\EdbZSp}}{}}%
\end{edb:lecture}%

\end{document}
\end{document}


