 |
|
|
|
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：25 、訪客IP：18.221.166.175
姓名 陳玉鴛(Yu-yuan Chen) 查詢紙本館藏 畢業系所 土木工程學系 論文名稱 多重疊近景影像匹配獲取房屋牆面紋理
(Acquisition of Building Facade Texture from Close-range Images)相關論文 檔案 [Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
- 本電子論文使用權限為同意立即開放。
- 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。
- 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) 房屋紋理的敷貼是建立仿真房屋模型中重要的步驟，而隨著數位
相機的普及，讓數位影像資料的獲取變的相當便利。針對已存在的積
木式房屋模型，本研究利用數位相機獲取大量資訊豐富的高重疊近景
影像，目的是利用高重疊影像獲得無遮蔽牆面紋理。但是所獲取的影
像資訊通常有前景遮蔽問題，因此，如何獲得無遮蔽牆面紋理是目前
需要克服的問題。
針對此問題，本研究所提出的處理方法可分為四個部份：(1)方
位求解，(2)產生三維點雲，(3)偵測遮蔽區，(4)影像填補。首先需進
行相機率定並解算影像方位參數。在影像方位解算乃利用已知之積木
模型取得控制點，配合於目標影像間觀測得的共軛點，解算影像的方
位參數。第二步，利用影像匹配求得共軛點的三維座標，產生三維點
雲。匹配時除了採用多視窗(Center-Left-Right)匹配外，本研究提出使
用影像分類作為額外的匹配指標，以提高匹配的正確率。接下來，以
影像的光譜及幾何的性質分析影像，以區塊的方式辨別房屋紋理及前
景遮蔽物。幾何方面，利用三維點雲所在位置判斷景物的距離；在光
譜方面，則以光譜資訊進行分類，使每個像元具有類別。在綜合光譜
及幾何條件後，可獲得無前景遮蔽的房屋紋理。最後是影像填補，第
三步驟後，我們擁有數張無遮蔽房屋紋理影像，使用副影像對於主影
像做填補，以獲得可能完整的房屋牆面紋理影像，但若有任一塊牆面
區塊無法被拍攝到，將無法產生完全無遮蔽的房屋牆面紋理。
由實驗結果得知，增加影像分類資訊輔助影像匹配，可以得到較
高的相關係數。而使用重新取樣及多視窗匹配法，可改善匹配視窗中
包含內容不同的部份。最終成果顯示，本方法可以移除前景遮蔽的區
域，並由其餘影像填補遮蔽區域，滿足視覺化之要求。
摘要(英) Texture mapping for building facades is an important task in
photorealistic building modeling. Following the popularity of digital
cameras, extraction of the object texture becomes convenient. The
close-range photogrammetry, thus, can acquire rich spatial and texture
information from high overlap images. Although those close-range
images can provide detail information, occlusion is still a problem to
overcome. To derive complete textures for a block building model, this
investigation proposes a scheme using high overlap images through
geometrical and spectral analyses.
There are four parts in the proposed scheme, namely, (1)
orientation modeling, (2) generation of 3D point clouds, (3) occlusion
detection, and (4) image compensation. First step calibrates the camera
and computes orientation parameters. We acquired high overlap images
with signalized targets for camera calibration. Then, the images were
acquired for test buildings. To examine the applicability of generating
ground control points (GCPs) from building models, a small number of
structure points were extracted. In the second part, we combine CLR
(Center-Right-Left) matching and image classification to derive reliable
conjugate points for 3D point clouding. Then, geometry and spectrum are
analyzed to separate the foreground from building surfaces. On the
geometry part, space intersection is employed to calculate the object
coordinates for conjugate points. In the spectrum analyses, image
classification is performed to determine the class for each pixel.
Combining geometry and spectrum characteristics, we detect the
foreground objects for removal. The last step is image compensation. The
occluded regions in a selected master image are then replaced by the
unhidden parts extracted from slave images.
The result shows higher correctness when CLR matching and
image classification are combined. Experimental results show that the
proposed method can detect occlusion part and replaced by the unhidden
parts extracted from other images.
關鍵字(中) ★ 房屋紋理
★ 近景影像
★ 影像匹配
★ 影像分類關鍵字(英) ★ Building Texture
★ Spectral Analysis
★ Image Matching
★ Close-range Images論文目次 摘要 ................................................. i
Abstract ............................................. ii
誌謝 ................................................. iii
目錄 ................................................. iv
圖目錄 ............................................... vii
表目錄 ............................................... x
第一章 前言........................................... 1
1.1 研究背景 ......................................... 1
1.2 文獻回顧 ......................................... 2
1.3 研究目的及內容 ................................... 5
第二章 研究方法 ...................................... 7
2.1 建立方位 ......................................... 8
2.1.1 相機率定 ....................................... 8
2.1.2 方位求解 ....................................... 10
2.2 影像匹配 ......................................... 11
2.2.1 特徵萃取 ....................................... 11
2.2.2 多影像匹配 ..................................... 13
2.2.2.1 整合式影像匹配 ............................... 15
2.2.2.2 影像分類 ..................................... 16
2.2.2.3 決定匹配視窗 ................................. 17
2.2.2.4 判斷匹配指標 ................................. 20
2.3 偵測遮蔽區 ....................................... 22
2.4 影像填補 ......................................... 24
第三章 研究成果與分析 ................................ 25
3.1 測試例資料 ....................................... 25
3.1.1 測試例一 ....................................... 26
3.1.2 測試例二 ....................................... 27
3.1.3 測試例三 ....................................... 28
3.2 相機資料 ......................................... 31
3.3 實驗成果 ......................................... 32
3.3.1 測試例一之成果 ................................. 32
3.3.2 測試例二之成果 ................................. 38
3.3.3 測試例三之成果 ................................. 47
3.4 成果分析 ......................................... 59
3.4.1 匹配視窗的改進 ................................. 59
3.4.2 影像分類的貢獻 ................................. 61
3.4.3 影像匹配成功率分析 ............................. 63
3.4.4 影像匹配正確率分析 ............................. 65
3.5 實驗總結 ......................................... 67
第四章 結論與建議 .................................... 69
參考文獻 ............................................. 72
參考文獻 徐偉城，1999，「空照彩色立體像對中人工建築物萃取之研究」，碩士論文，國立中央大學土木工程研究所。
林后駿，2005，「三維房屋模型實景紋理影像製作與敷貼之研究」，碩士論文，國立中央大學土木工程研究所。
Canny, J.,1986. A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, pp. 679-698.
Catmull, E.E.,1975. Computer display of curved surfaces. Proceedings of the IEEE Conference on Computer Graphics, Pattern Recognition, and Data Structure, pp. 11–17.
Chang, K.,2010. Introduction to Geographic Information Systems,5thed. McGraw Hill, Boston, MA.
Efros, A.and Leung, T.,1999.Texture synthesis by non-parametric sampling, in Proc. Int. Conf. Computer Vision, Kerkyra, Greece, pp.1033-1038.
Foerstner, W.and Guelch, E., 1987. A fast operator for detection and precise location of distinct points, corners and centers of circular features. Proceedings of ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, pp. 281-305.
Fraser, C. S., 1997. Digital camera self-calibration, ISPRS Journal of Photogrammetry & Remote Sensing, 52:149-159.
Fraser, C.S., 2009, A hybrid measurement approach for close-range photogrammetry, ISPRS Journal of Photogrammetry and Remote Sensing,64(3):328-333.
Han, J.H. and Park , J.S., 2000. Contour matching using epipolar geometry, IEEE Transactions On Pattern Analysis And Machine Intelligence,22(4):358-370.
Harris, C. and Stephens, M., 1988, A combined corner and edge Detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147-151.
Lowe, D.G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision,60(2):91–110.
Lue, Y., 1988. Interest operator and fast implementation. In: ISPRS Congr.Kyoto, Int. Arch. Photogramm. Remote Sensing XXVII, B3:491–500.
Matikainen, L., Hyyppa, J.,and Kaartinen, H., 2004. Automatic detection of changes from laser scanner and aerial image data for updating building maps. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(B2): 434-439.
Moravec, H.P.,1977. Towards Automatic Visual Obstacle Avoidance,International Joint Conference of Artificial Intelligence,Tokyo, Vol.1
Oliveira, M., Bowen, B., McKenna, R., and Chang, Y.S., 2001. Fast Digital Image Inpainting, Appeared in the Proceedings of the International Conference on Visualization, Imaging and Image Processing (VIIP 2001), Marbella, Spain.September 3-5.
Otto, G. P., and Chau, T. K. W.,1989. A region-growing algorithm for matching of terrain images. Image Vision Computing,7(2):83-94.
Photometrix, 2011. Iwitness and Iwitnesspro, Jan. 10, 2011, http://www.photometrix.com.au/.
Pratt, W.K.,1991.Digital Image Processing, 2nd ed., Wiley, New York.
Rosten, E., and Drummond, T.W., 2006. Machine learning for high-speed corner detection. The 9th European Conference on Computer Vision, ECCV 2006. Lecture Notes in Computer Vision, vol. 3951. Springer, pp. 430-443.
Rottensteiner, F., and Briese, Ch.,2002. A new method for building extraction in urban areas from high-resolution LIDAR data.ISPRS, XXXIII:295-301,Graz,Austria.
Rottensteiner, F., Trinder, J., Clode, S,. and Kubik, K., 2003. Building detection using LIDAR data and multi-spectral images, in: Proc. APRS Conference on Digital Image Computing: Techniques and Applications, DICTA, Sydney, December 2003, 2:673-682.
Smith, S.M., and Brady, J.M., 1997. SUSAN - A new approach to low level image processing. International Journal of Computer Vision. 23(1):45-78.
Tasi, F., and Lin , H.C., 2007. Polygon-base texture mapping for cyber city 3D building modelings. International Journal of Geographical Information Science,21(9):965-981.
Wolf, P.R, and Dewitt , B.A.,2002.Elements of photogrammetry :with applications inGIS,3rd ed. Wolf, P.R, Dewitt, B.A.McGrawHill Book Co.
Wu, J., Zhou , G., Ding , F. L., and Liu , Z., 2007. Automatic retrieval of optimal texture from aerial video for photo-realistic of street environment. Proceedings of the fourth International Conference on Image and Graphics, pp. 943-947.
Zhang, L., and Gruen , A., 2006. Multi-image matching for DSM generation from IKONOS imagery. ISPRS Journal of Photogrammetry and Remote Sensing,60(3):195-211.
指導教授 陳良健(Liang-Chien Chen) 審核日期 2012-7-21 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit