WEKO3
アイテム
Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern
https://doi.org/10.18997/00004065
https://doi.org/10.18997/00004065e60af2ca-9358-44ad-a613-286eb35a1a45
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
D-213_kou_k_354.pdf (11.5 MB)
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| アイテムタイプ | 学位論文 = Thesis or Dissertation(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2014-07-30 | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||
| 資源タイプ | doctoral thesis | |||||||
| タイトル | ||||||||
| タイトル | Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern | |||||||
| 言語 | en | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 著者 |
Hossain, Shahera
× Hossain, Shahera
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ― based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ― Signal and Image Processing Institute) Rotations Texture dataset. | |||||||
| 目次 | ||||||||
| 内容記述タイプ | TableOfContents | |||||||
| 内容記述 | chapter 1 introduction||chapter 2 features for texture analysis||chapter 3 in-depth analysis of texture databases||chapter 4 analysis of features based on co-occurrence image matrix||chapter 5 categorization of features based on co-occurrence image matrix||chapter 6 texture recognition based on diagonal-crisscross local binary pattern||chapter 7 conclusions and future work | |||||||
| 備考 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 九州工業大学博士学位論文 学位記番号:工博甲第354号 学位授与年月日:平成25年9月27日 | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | LBP | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Features | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Texture | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Matrix | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | DCLBP | |||||||
| アドバイザー | ||||||||
| 芹川, 聖一 | ||||||||
| 学位授与番号 | ||||||||
| 学位授与番号 | 甲第354号 | |||||||
| 学位名 | ||||||||
| 学位名 | 博士（工学） | |||||||
| 学位授与年月日 | ||||||||
| 学位授与年月日 | 2013-09-27 | |||||||
| 学位授与機関 | ||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||
| 学位授与機関識別子 | 17104 | |||||||
| 学位授与機関名 | 九州工業大学 | |||||||
| 学位授与年度 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 平成25年度 | |||||||
| 出版タイプ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| ID登録 | ||||||||
| ID登録 | 10.18997/00004065 | |||||||
| ID登録タイプ | JaLC | |||||||
