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研究生:陳秀紋
研究生(外文):Shio Wen Chen
論文名稱:使用Google街景圖與SURF及顏色特徵之城市特色建築物辨識系統
論文名稱(外文):A Distinctive Urban Buildings Recognition System Using the Google Street View with SURF and Color Features
指導教授:張厥煒張厥煒引用關係
指導教授(外文):Chueh-Wei Chang
口試委員:奚正寧楊士萱
口試委員(外文):Chen-Ning HsiShih-Hsuan Yang
口試日期:2012-07-19
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:資訊工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:47
中文關鍵詞:建築物辨識SURF物件辨識Google Street View
外文關鍵詞:Building RecognitionSURFObjet RecognitionGoogle Street View
相關次數:
  • 被引用被引用:5
  • 點閱點閱:400
  • 評分評分:
  • 下載下載:66
  • 收藏至我的研究室書目清單書目收藏:0
建築物不管在藝術文化、歷史意義以及民生需求,在世界各地都占有其重要的地位。若是可以從中找出建築物特有的特徵,對於藝術文化以及建築史的演進的數位典藏,可以有其助益。特別是在現在智慧型手持裝置的盛行,若是可以從手持裝置的攝影機捕捉到身邊的建築物影像,藉由建築物的辨識得到相關的資訊,如果可以做出一些有趣的應用,對於文化推廣以及觀光產業有很大的幫助。近年來對於物件辨識的方法發展迅速,以及Google Street View的出現,世界上每個角落都有了最完整的影像資料,對於建築物的辨識又是一大利器。
本論文藉由對於台灣都市中建築物的觀察,使用SURF以及顏色等特徵透過影像處理的方式從Google Street View中擷取出特色建築物的特徵,針對不同角度、光線、非完整建築物的建築物照片進行比對辨識。


In the sense of culture, art, and history meaning, buildings have played an important role in our lives. If we can retrieve unique features that can describe a building, it might have some benefits for architecture history or digital resources of architecture. As the popularity of smart mobile devices, if we could have some interesting application for getting information of buildings around user, captured in any direction and view, it must be a great help for the promotion of culture and tourism industry.
In this paper, I propose a system using SURF and color features for distinctive buildings in Taipei. This system using Google Street View’s image for feature learning database .Based on the research of buildings’ characteristics in Taiwan, the recognition system can identify buildings robustly in different scales, rotation, and partial building’s image in this system.


摘要 I
ABSTRACT II
ACKNOWLEDGMENTS III
TABLE OF CONTENTS IV
LIST OF TABLES VI
LIST OF FIGURES VII
CHAPTER1 INTRODUCTION 1
1.1 RESEARCH MOTIVATION 1
1.2 RESEARCH PURPOSE 2
1.3 THESIS STRUCTURE 3
CHAPTER2 RELATED WORKS 4
2.1 BUILDING RECOGNITION 4
2.2 GOOGLE STREET VIEW 5
2.3 SIFT AND SURF FEATURES 6
2.4 LIMITATIONS 9
CHAPTER3 SYSTEM ARCHITECTURE 12
3.1 SYSTEM OVERVIEW 12
3.2 SYSTEM ARCHITECTURE DISCRIPTION 13
CHAPTER4 FEATURE LEARNING 15
4.1. GOOGLE STREET VIEW IMAGE 16
4.2. BUILDING EXTRACTION 19
4.3. SURF FEATURE EXTRACTION 24
CHAPTER5 BUILDING RECOGNITION 26
5.1 COLOR COMPARISON 27
5.2 IMAGE NORMALIZATION 27
5.3 SURF FEATURE MATCHING 28
CHAPTER6 EXPERIMENTAL RESULTS 31
6.1 SYSTEM ENVIRONMENT 31
6.2 SYSTEM USER INTERFACE AND FUNCTIONS 32
6.3 EXPERIMENTAL RESULT 33
6.3.1 The Cases of Successful Recognition 34
6.3.2 The Cases of Wrong Recognition 40
CHAPTER7 CONCLUSION AND FUTURE WORKS 43
7.1 CONCLUSION 43
7.2 FUTURE WORKS 44
REFERENCE 45


[1]Levitt, S., Aghdasi, F., “Texture Measures for Building Recognition in Aerial Photographs,” Proc. of South African Symposium on Communications and Signal Processin, 1997, pp.75-80.
[2]Michaelsen, E. FGAN-FOM, Ettlingen ,Doktorski, L., Soergel, U., and Stilla, U., “Perceptual Grouping for Building Recognition in High-resolution SAR Images using the GESTALT-System,” IEEE Urban Remote Sensing Joint Event, 2007, pp.1-6.
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[5]Keyan Ren, Hanxu Sun, Qingxuan Jia, and Jianbo Shi, “Building Recognition from Aerial Images Combining Segmentation and Shadow,” IEEE Intl. Conf. on Intelligent Computing and Intelligent Systems, vol.4, 2009,pp.578-582.
[6]Byungsoo. Lim, and Joonoo. Kim, “Efficient Database Reduction Method of Building Recognition using Global Positioning System On Mobile Device,” IEEE Intl. Symposium on Wireless Pervasive Computing, 2009, pp.1-5.
[7]Yu-Chia Chung, Tony X. Han, and Zhihai He, “Building Recognition Using Sketch-Based Representations and Spectral Graph Matching,” Intl. Conf. on Computer Vision, 2009, pp. 2014-2020.
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[10]Di Lillo, A., Daptardar, A., Thomas, K., Storer, J.A., Motta, G. “Applications Of Compression To Content Based Image Retrieval and Object Recognition,” First Intl. Conf. on Data Compression, Communications and Processing, 2011, pp.179-189.
[11]T. S. H. Shao and L. V. Gool, “Zurich buildings database for image based recognition,” Technical Report No. 260, Swiss Federal Institute of Technology, May 2003.
[12]Wiki Google Street View http://en.wikipedia.org/wiki/Google_Street_View
[13]http://maps.google.com.tw/intl/zh-TW/help/maps/streetview/technology/cars-trikes.html
[14]D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int.l Conf. on Computer Vision, vol. 60, 2004, pp. 91-110.
[15]H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded Up Robust Features,” Proc. of European Conf. on Computer Vision, 2006, pp. 404-417.
[16]Rainer Lienhart and Jochen Maydt, “An Extended Set of Haar-like Features for Rapid Object Detection,” IEEE Intl. Conf. Image Processing ,vol. 1, 2002, pp. I-900 – I-903.
[17]L. Juan and O. Gwun, “A Comparison of SIFT, PCA-SIFT and SURF,” Intl. Journal of Image Processing, vol.3, 2009, pp. 143-152.
[18]http://jamiethompson.co.uk/web/2010/05/15/google-streetview-static-api/
[19]http://www.chrisevansdev.com/computer-vision-opensurf.html
[20]K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, 2005, pp. 1615–1630.
[21]Yin-Hsi Kuo, Wen-Huang Cheng, Hsuan-Tien Lin, and Winston H. Hsu, “Unsupervised Semantic Feature Discovery for Image Object Retrieval and Tag Refinement”, IEEE Trans. on Multimedia, vol. 14, 2012, pp.1079-1090.


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