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研究生(外文):Huang, Chi-Ming
論文名稱(外文):Content-Based Building Image Retrieval
指導教授(外文):Chen, Zen
外文關鍵詞:building image retrievalMSERZernike Momentkd-treeZuBud
  • 被引用被引用:2
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本論文目的在使用影像區域特徵來建立一個建築物影像檢索系統。此檢索系統分成資料庫與查詢兩個部份,資料庫部份按照處理順序又可分為三個步驟,第一步驟使用可抗視角變化的Maximally Stable Extremal Region做特徵區域擷取;第二步驟使用旋轉不變的phased-based Zernike Moment做特徵區域描述;第三步驟使用kd-tree建立特徵向量的索引。建立資料庫時,使用同一棟建築物相鄰的影像特徵互相比對,去除不穩定出現的特徵區域,並使用Density-Based Spatial Clustering of Applications with Noise分群法,以減少資料庫中存在的儲存重覆特徵問題。查詢部分採用kd-tree找最近點與鄰近點的便利性,以直觀的投票機制找出資料庫中與查詢影像最相似的建築物。
The goal of this thesis research is to construct a building image indexing and retrieval system. This system consists of two parts: the database organization (indexing) and the query part (retrieval). The database part is further composed of three modules. In the first module, view-invariant feature detection, Maximally Stable Extremal Region (MSER), is used to extract the regions of interest. In the second module, the phased-based Zernike Moment is used to describe the regions. In the third module, a kd-tree structure is used to establish the index of Zernike Moment feature vectors. When constructing the database, in order to eliminate the unstable regions, a trick of comparison of the features extracted from the neighboring views of the same building is used. To reduce the problem of redundancy, the clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is used. In the query part, the kd-tree provides a convenient way to find the nearest neighbor. And then an intuitive voting mechanism is used to find the building from the database which is most similar to the query image.
第一章 緒論 1
1.1 研究動機與目標 1
1.2 問題陳述 1
1.3 相關研究 2
1.4 論文架構 3
第二章 單張影像特徵偵測與描述 5
2.1 MSER特徵區域擷取 5
2.1.1 MSER概述 6
2.1.2 過濾不適用於辨識的MSER橢圓區域 8
2.2 Zernike Moment特徵區域描述 11
2.2.1 Zernike Moment概述 12
2.2.2 Zernike Moment相似度的衡量方法 13
第三章 建築物影像與特徵資料庫建立 14
3.1 建築物影像拍攝 14
3.1.1 建築物資料庫影像拍攝考慮因素 16
3.1.2 建築物查詢影像拍攝考慮因素 17
3.2 建築物資料庫影像特徵偵測與描述 18
3.2.1 使用相鄰影像去除不穩定的建築物影像特徵區域 18
3.2.2 建築物多面貌的表示法 22
3.3 使用DBSCAN做建築物資料庫影像的特徵區域分群 22
3.3.1 DBSCAN概述 23
3.3.2 運用DBSCAN對影像特徵區域的分群 24
3.4 建築物影像特徵的kd-tree資料庫建立 26
第四章 建築物影像檢索機制 29
4.1 kd-tree最近點搜尋與範圍搜尋 29
4.1.1 利用kd-tree找強度最接近的特徵向量 29
4.1.2 利用kd-tree範圍搜尋找強度接近的特徵向量 31
4.2 建築物檢索結果的產生 32
第五章 辨識實驗結果與評估 33
5.1 交大建築資料庫的辨識結果 33
5.1.1 交大建築資料庫介紹 33
5.1.2 交大建築物影像不同拍攝因素下的辨識結果 36
5.1.3 時間和空間複雜度評估 36
5.2 ZuBud建築資料庫的辨識結果 39
5.2.1 ZuBud建築資料庫介紹 39
5.2.2 ZuBud建築資料庫的辨識結果 39
第六章 結論與未來發展 41
參考文獻 42

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