博碩士論文 100522058 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:21 、訪客IP:18.221.174.248
姓名 歐陽寧(Ning, Ouyang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 影像特徵點匹配應用於景點影像檢索
(Image Feature Point Matching on Landmark Image Retrieval Applications)
相關論文
★ 影片指定對象臉部置換系統★ 以單一攝影機實現單指虛擬鍵盤之功能
★ 基於視覺的手寫軌跡注音符號組合辨識系統★ 利用動態貝氏網路在空照影像中進行車輛偵測
★ 以視訊為基礎之手寫簽名認證★ 使用膚色與陰影機率高斯混合模型之移動膚色區域偵測
★ 影像中賦予信任等級的群眾切割★ 航空監控影像之區域切割與分類
★ 在群體人數估計應用中使用不同特徵與回歸方法之分析比較★ 以視覺為基礎之強韌多指尖偵測與人機介面應用
★ 在夜間受雨滴汙染鏡頭所拍攝的影片下之車流量估計★ 自動感興趣區域切割及遠距交通影像中的軌跡分析
★ 基於回歸模型與利用全天空影像特徵和歷史資訊之短期日射量預測★ Analysis of the Performance of Different Classifiers for Cloud Detection Application
★ 全天空影像之雲追蹤與太陽遮蔽預測★ 在全天空影像中使用紋理特徵之雲分類
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 隨著科技的發展,隨身攜帶的設備像是智慧型手機與平板非常普遍,這些設備通常都具備著全球地位系統(GPS)能夠定位出現在大概的位置,這對旅客是非常有用的資訊,然而,當人們在旅行時,光憑GPS的位置資訊並不能給予正確且明確的景點資訊,有時當旅客站在遠方觀察景點時,同時會觀察到許多景點在同一位置上,處在這種情形之下,當旅客希望取得某個景點資訊,必須查詢所有附近的景點試著取得想要的資訊,隨著智慧性設備都具備著相機的功能,我們提出一個應用程式利用拍照取得影像結合GPS取得地點位置資訊快速地回應使用者想要的景點資訊。使用者可以輕易地照相與點擊按鈕查詢相關知名的旅遊網站。
本系統利用Google Place API取得周邊地點的名字與類型,首先過濾這些地點取得候選的景點,接著使用候選景點的名字,當作關鍵字去大眾搜尋引擎取得景點影像,對回傳的影像會使用局部特徵匹配技術作確認。本論文使用Speeded Up Robust Feature (SURF)特徵與Oriented FAST and Rotated BRIEF (ORB)特徵,本系統藉由影像特徵匹配技術去確認出正確的景點影像,我們也使用FLANN函式庫建立隨機k維樹(Randomized k-d tree),確認景點名字後,本系統可以使用正確的景點名字查詢知名旅遊網站,並將搜尋結果呈現給使用者。我們做了許多實驗去比較不同特徵還有不同參數的設定,並展示本系統的可靠與有效性。
摘要(英) With the development of technology, portable devices such as smart phones and tablets have become prevalent. These devices are often equipped with Global Positioning System (GPS) and are able to locate its current approximate location. Such information is very useful for travelers. However, when people are travelling, the GPS readings alone could not accurately give the exact landmark information to the device carriers. Sometimes the travelers would observe a landmark from a distance. In addition, multiple landmarks could be observed at the same place. Under such circumstances, if the travelers wish to retrieve the landmark information, they would need to look up all the landmarks nearby and try to get the information they are looking for. Since the portable devices are also equipped with cameras, we propose an application that can utilize the images captured by the cameras and the location information acquired by the GPS readings and rapidly return the desired landmark information to the users. The user can easily take a photo and touch a button to search for the correct landmark information from famous travel websites.
The proposed system takes advantage of the Google place API and gets the names and types of the nearby places of the current GPS reading. The names are filtered first to get the landmark candidates. Then the keywords of these landmark candidates are used to query the image database of the public search engines. The returned images are then confirmed by the system using local feature matching techniques. In the thesis, we use Speeded Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) features. The system analyzes these images by feature matching to confirm the correct image of the landmark. We also use FLANN library to construct randomized k-d tree. After confirming the landmark name, the system would query the famous travel websites using the correct landmark name and return the search results to the users. We have performed experiments to compare the performance of different features and parameter settings. We have also demonstrated the feasibility and effectiveness of the proposed application system.
關鍵字(中) ★ 影像特徵
★ 景點影像檢索
關鍵字(英) ★ image feature
★ landmark image retrieval
論文目次 摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VII
第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 系統使用情境與流程 5
1.4 論文架構 8
第二章 特徵方法回顧 9
2.1 SURF(Speed up Robust Feature) 9
2.1.1 積分影像法(Integral Image) 9
2.1.2 SURF 基於 Hessian-Matrix 的特徵點偵測 10
2.1.3 SURF尺度空間表示 12
2.1.4 SURF定位有興趣之特徵點 13
2.1.5 SURF方向指派 14
2.1.6 SURF特徵描述子 15
2.2 ORB特徵回顧 ( oFAST + rBRIEF) 16
2.2.1 FAST 特徵偵測 16
2.2.2 由中心強度(centroid intensity)決定方向 18
2.2.3 增加旋轉向量的BRIEF描述子( Steered BRIEF) 18
2.2.4 學習好的2位元特徵 20
第三章 系統說明與影像相似度 21
3.1 系統說明 21
3.1.1 取得經緯度資訊 21
3.1.2 取得地點資訊與過濾 22
3.1.3 取得地點圖片 25
3.2 系統介面功能說明 26
3.3 影像特徵匹配與驗證方法 28
3.3.1 匹配與驗證方法 30
3.3.2 極線幾何基本矩陣說明 32
3.3.3 隨機抽樣一致演算法(RANSAC)驗證 35
3.4 FLANN建立k-d tree與驗證方法 37
3.5 影像比對流程 40
第四章 實驗結果與分析 42
4.1 系統環境與測試資料 42
4.2 實驗結果與分析 43
第五章 結論與未來研究方向 52
參考文獻 53
附錄A JNI (Java Native Interface) 59
JNI 使用說明 59
中文字串處理 61
使用OpenCV注意事項 61
附錄B 可交換圖像文件(EXIF) 62
附錄C J SON(JavaScript Object Notation) 64
取得資料方法 64
JSON 資料格式分析 65
參考文獻 [1] ”智慧型手機,” [Online] Available:
http://www.cc.ntu.edu.tw/chinese/epaper/0008/20090320_8004.htm
[2] “平板電腦,” [Online]” Available:
http://tw.asus.com/Eee/Eee_Pad/Eee_Pad_Slider_SL101/
[3] “Flickr,” [Online] Available: http://www.flickr.com
[4] Y.-T. Zheng, M. Zhao, Y. Song, H. Adam, U. Buddemeier, A. Bissacco, F. Brucher, T.-S. Chua and H. Neven, “Tour the world: building a web-scale landmark recognition engine,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1082-1092, 2009.
[5] S. Gammeter, L. Bossard, T. Quack and L. V. Gool, “I know what you did last summer: object-level auto-annotation of holiday snaps,” 2009 IEEE 12th International Conference on Computer Vision (ICCV), pp. 614-621, 2009.
[6] J. Philbin, O. Chum, M.Isard, J. SIvic, and A. Zisserman, “Object retrieval with large vocabularies and fast spatial matching,” IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 1-8, 2007.
[7] O. Chum and J. Matas, “Large Scale Discovery of Spatially Related Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 32, no. 2 , pp. 371-377, 2010.
[8] J. Philbin, O. Chum, M. Isard, J. Sivic and A. Zisserman, “Lost in Quantization: Improving Particular Object Retrieval in Large Scale Image Databases,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2008.
[9] Y. Li, D. J. Crandall, D. P. Huttenlocher, “Landmark Classification in Large-scale Image Collections,” 2009 IEEE International Conference on Computer Vision (ICCV), pp. 1957-1964, 2009.
[10] J. Sivic and A. Zisserman, “Video Google: A Text Retrieval Approach to Object Matching in Videos,” Ninth IEEE International Conference on Computer Vision (ICCV), pp. 1470-1477, 2003.
[11] G. Takacs, V. Chandrasekhar, N. Gelfand, Y. Xiong, W.-C. Chen, T. Bismpigiannis, R. Grzeszczuk, K. Pulli and B. Girod, “Outdoors Augmented Reality on Mobile Phone using Loxel-Based Visual Feature Organization,” ACM International Conference on Multimedia Information Retrieval (MIR), pp. 427-434, 2008.
[12] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision (IJCV), vol. 60, no. 2, pp. 91–110, 2004.
[13] A. Mikulfk, M. Perdoch, O. Chum and J. Matas, “Learning a Fine Vocabulary,” Proc. European Conference on Computer Vision(ECCV), pp. 1-14, 2010.
[14] C. R. Dance, L. Fan, J. Willamowski and C. Bray, “Visual Categorization with Bags of Keypoints,” Proc. European Conference on Computer Vision(ECCV), pp. 1-22, 2004.
[15] H. J´egou, M. Douze and C. Schmid, “Improving bag-of-features for large scale image search,” International Journal of Computer Vision (IJCV), vol. 87, no. 3 pp. 316-336, 2010.
[16] Z. Wu, Q. Ke, M. Isard, and J. Sun, “Bundling Features for Large Scale Partial-Duplicate Web Image Search,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 25-32, 2009.
[17] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. “Speeded-Up Robust Features (SURF),” Proc. European Conference on Computer Vision(ECCV), pp. 346-359, 2006.
[18] M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “BRIEF: Binary Robust Independent Elementary Features,” Proc. European Conference on Computer Vision (ECCV), pp. 778-792, 2010.
[19] E. Rublee, V. Rabaud, K. Koonlige, and G. Bradski, “ORB: an efficient alternative to SIFT or SURF,” 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564-2571, 2011.
[20] S. Leutenegger, M. Chli, and R. Y. Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints,” 2011 IEEE International Conference on Computer Vision(ICCV), pp. 2548-2555, 2011.
[21] A. Alahi, R. Ortiz and P. Vandergheynst, “FREAK: Fast Retina Keypoint,” 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510-517, 2012.
[22] M. Muja and D. G. Lowe, “Fast Matching of Binary Features,” 2012 Ninth Conference on Computer and Robot Vision(CRV), pp. 404-410, 2012.
[23] R. Arandjelovi´c and A. Zisserman, “Three tings everyone should know to improve object retrieval,” 2012 IEEE Computer Conference on Computer Vision and Pattern Recognition (CVPR), pp.2911-2918, 2012.
[24] C. Burges, “A tutorial on support vector machines for pattern recognition,“ Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998.
[25] C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, 2011.
[26] D. C. Hauagge and N. Snavely, “Image Matching using Local Symmetry Features,” 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
[27] J. Kim and K. Grauman, “Boundary Preserving Dense Local Regions,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1553-1560, 2011.
[28] J. Wu and J. M. Rehg, “CENTRIST: A Visual Descriptor for Scene Categorization,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 33, no. 8, pp. 1489-1501, 2011.
[29] C. Silpa-Anan and R. Hartley, “Optimised KD-trees for fast image descriptor matching,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, 2008.
[30] M. Muja and D. G. Lowe, “Fast approximate nearest neighbors with Automatic algorithm configuration,” in International Conference on Computer Vision Theory and Applications (VISAPP’09), 331-340, 2009
[31] R. Laganière. “OpenCV 2 Computer Vision Application Programming Cookbook,” Packt Publishing 2011.
[32] P. M. Neila, J. G. Miró, J. M. Buenaposada and L. Baumela, “Improving RANSAC for fast Landmark Recognition,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1-8, 2008.
[33] J. Shotton, J.Winn, C. Rother, and A. Criminisi, “Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context,” International Journal of Computer Vision (IJCV), Vol. 81, no. 1, pp. 2-23, 2009.
[34] J. Shotton, M. Johnson, and R. Cipolla, “Semantic Texton Forests for Image Categorization and Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2008.
[35] R. Edward, and D. Tom, “Machine learning for high-speed corner detection,” Proc. European Conference on Computer Vision (ECCV), pp. 430-443, 2006.
[36] P.L. Rosin, ”Measuring corner properties,” International journal of Computer Vision and Image Understanding, Vol. 73, no. 2, pp. 291 – 307,1999.
[37] C. Harris and M. Stephens, “A Combined Corner and edge Detector,” Proceedings of the 4th Alvey Vision Conference, pp. 147-151, 1988.
[38] “metadata-extractor”, [Online] Available:
https://code.google.com/p/metadata-extractor/
[39] “Google place API”, [Online] Available:
https://developers.google.com/places/documentation/?hl=zh-TW
[40] ”Great-circle distance” [Online] Available:
http://en.wikipedia.org/wiki/Great-circle_distance
[41] Z. Zhang, “Determining the Epipolar Geometry and its Uncertainty: A Review,” International Journal of Computer Vision(IJCV), Vol. 27, no. 2, pp. 161-198, 1998.
指導教授 鄭旭詠(Hsu-Yung Cheng) 審核日期 2013-7-1
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明