Effectiveness of Genetic Multistep Search in Unsupervised Design of Morphological Filters for Noise Removal

• Item type: Trans(1)
• 公開日: 2010-10-25
• タイトル: Effectiveness of Genetic Multistep Search in Unsupervised Design of Morphological Filters for Noise Removal
• タイトル / 言語: en
• タイトル: Effectiveness of Genetic Multistep Search in Unsupervised Design of Morphological Filters for Noise Removal
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: オリジナル論文
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Kansai University
• 著者所属: Kansai University
• 著者所属: Graduate School of Engineering, Hiroshima University
• 著者所属(英): en
• 著者所属(英): Kansai University
• 著者所属(英): en
• 著者所属(英): Kansai University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Hiroshima University
• 著者名: Yoshiko, Hanada; Mitsuji, Muneyasu; Akira, Asano
• 著者名(英): Yoshiko, Hanada; Mitsuji, Muneyasu; Akira, Asano
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this paper, the effectiveness of deterministic Multi-step Crossover Fusion (dMSXF) and deterministic Multi-step Mutation Fusion (dMSMF), which are types of genetic multistep searches based on a neighborhood search mechanism, in solving an unsupervised design problem of suitable structuring elements (SEs) of a morphological filter is shown. In our previous work, it was shown that dMSXF and dMSMF are very effective for solving combinatorial optimization problems, particularly on problems for which the landscape is an AR(1) landscape observed in the NK model. In addition, their effectiveness for reproduction mechanisms to obtain the offspring was shown to be retained with increasing level of epistasis. In this paper, we show that a characteristic of the AR(1) landscape is observed in an objective function for the unsupervised design of SEs, and superior search performances of both dMSXF and dMSMF for conventional crossover are shown. The processing results of the obtained SEs are also compared with those of conventional filters used for impulse noise removal.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this paper, the effectiveness of deterministic Multi-step Crossover Fusion (dMSXF) and deterministic Multi-step Mutation Fusion (dMSMF), which are types of genetic multistep searches based on a neighborhood search mechanism, in solving an unsupervised design problem of suitable structuring elements (SEs) of a morphological filter is shown. In our previous work, it was shown that dMSXF and dMSMF are very effective for solving combinatorial optimization problems, particularly on problems for which the landscape is an AR(1) landscape observed in the NK model. In addition, their effectiveness for reproduction mechanisms to obtain the offspring was shown to be retained with increasing level of epistasis. In this paper, we show that a characteristic of the AR(1) landscape is observed in an objective function for the unsupervised design of SEs, and superior search performances of both dMSXF and dMSMF for conventional crossover are shown. The processing results of the obtained SEs are also compared with those of conventional filters used for impulse noise removal.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464803
• 書誌情報: 情報処理学会論文誌数理モデル化と応用（TOM）; 巻 3, 号 3, p. 154-165, 発行日 2010-10-25
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7780
• 出版者 / 言語: ja
• 出版者: 情報処理学会