影像修補技術於牆面紋理影像遮蔽去除之應用

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：8

、訪客IP：3.144.17.45

姓名

陳品學(Ping-Shen Chen) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

影像修補技術於牆面紋理影像遮蔽去除之應用
(OCCLUSION REMOVAL OF FA?ADE TEXTURE WITH CONSTRAINED IMAGE INPAINTING)

相關論文

★ 三維房屋模型實景紋理影像製作與敷貼之研究	★ 紋理輔助高解析度衛星影像分析應用於偵測入侵性植物分布之研究
★ 利用高光譜影像偵測外來植物-以恆春地區銀合歡為例	★ 以視訊影像進行三維房屋模型實景紋理敷貼之研究
★ 區塊式Level of Detail地景視覺模擬之研究	★ 高光譜影像立方體紋理特徵之三維計算
★ 漸變式多重解析度於大型地景視覺模擬之應用	★ 區塊式LOD網格細化於大型地形視覺模擬之應用
★ 多層次精緻度三維房屋模型之建置	★ 高光譜影像立方體於特徵空間之三維紋理計算
★ 結合遙測影像與GIS資料以資料挖掘技術進行崩塌地辨識-以石門水庫集水區為例	★ 利用近景影像提高三維建物模型之細緻化等級
★ 以地面及空載光達點雲重建複雜物三維模型	★ 高精緻度房屋模型結合蟻群演算法於室內最佳路徑選擇之應用
★ 二次微分法於空載全波形光達之特徵萃取與地物分類	★ 雲線擬合於全波形光達之特徵萃取與地物分類

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]

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)

在三維數碼城市中，為了更為符合真實世界，通常都會敷貼牆面紋理影像在模型上，然而在近景拍攝的過程中經常會受到一些樹木、車子、及其他物體的遮蔽影響。因此，有效地去除及修補遭遮蔽的牆面紋理圖像是一個值得研究的課題。數位影像修復是一種移除遮蔽並填補數位影像的有效方法，然而將現有的影像修補技術直接應用於牆面紋理，對於遮蔽的處理並不理想。因此本研究研發一個改良的影像修補技術以處理受遮蔽的牆面紋理，目的是將選擇的遮蔽物移除並藉由此技術找出可靠的紋理回填受損的區域。本研究所研發的影像修補結合了等照度面、法向量、可靠度計算等，計算影像填補的優先順序，並合理地修復受遮蔽的紋理影像。
與現有的影像修補相比，本研究提出的修補技術是基於房屋牆面紋理上的明顯結構，如樑、柱、窗子等以更合理的修復遮蔽。一些重要的參數如搜尋視窗、搜尋範圍也經深入探討以測試影像修補的成果；此外由於牆面紋理有沿著特定方向的特性，因此限定影像修補沿著特定的方向測試其成效。本研究所研發的改良式影像修補技術以真實牆面紋理影像進行測試，並確立影像修補參數的設定。最後，以均方根誤差和結構相似度等指標，進行牆面紋理影像遮蔽修補成果之量化評估。本研究測試不同類型之房屋牆面紋理修復，並以多視角呈現建物群體影像修復，實作成果顯示本研究所提出以房屋牆面紋理上明顯結構特徵為基本單元之方式，可有效修復受遮蔽的牆面紋理影像。

摘要(英)

In three dimension digital city modeling, photographic fa?ade texture images are commonly attached to building faces in order to generate more realistic building models and scenes. However, there are usually occlusions from trees, cars and other foreign objects in close-ranged fa?ade photographs. Therefore, it is worthwhile to develop effective methods for efficiently correcting the occlusions in fa?ade texture images. Image inpainting is an effective approach to remove unwanted objects and fill holes in digital images. However, directly applying existing, general-purpose digital inpainting algorithms to the correction of fa?ade texture images with occlusions may not produce satisfactory results. This research developed a constrained image inpainting algorithm specifically designed for the correction of occluded fa?ade texture images. The objective is to remove selected occlusions of fa?ade texture images and restore the damaged texture blocks with reasonable textures identified with the developed constrained inpainting algorithm. The developed inpainting algorithm combines isophote, normal vector and confidence terms to calculate the best fill order of inpainting.
In comparison with existing inpainting approaches, it can restore the occluded texture blocks more reasonably based on fa?ade structures such as pillars, girders and window frames. A few parameters, such as window size and search area of inpainting were also investigated to better understand their effects on the inpainting results. Special constrains on the direction of inpainting order, such as top-down and bottom-up were also tested to understand their effects because the features of texture fa?ade were presumed to be along a particular direction. The developed constrained inpainting algorithms were applied to real building fa?ade images to valid their performance and to identify appropriate inpainting parameters for correcting fa?ade texture occlusions. Finally, the most widely used full-reference quality metric mean square error (MSE) and structural similarity index (SSIM) were employed to quantitatively evaluate the inpainting results.
Several complex building models are used to test the constrained image inpainting algorithms. Experimental results demonstrate that the proposed method based on fa?ade structures is effective in restoring occlusion of building fa?ade texture images. In addition, the experimental results of building fa?ade texture images with special structures are also validated, proving that the proposed approach can restore more reasonable visualization results.

關鍵字(中)

★ 遮蔽移除
★ 影像修復
★ 牆面紋理
★ 影像修補
★ 等照度面

關鍵字(英)

★ Isophote
★ Inpainting
★ fa?ade texture
★ image restoration
★ occlusion removal

論文目次

摘要 I
Abstract III
致謝 V
目錄 VI
表目錄 IX
圖目錄 IX
第一章緒論 1
1-1 背景介紹 1
1-2 研究動機與目的 2
第二章文獻回顧 4
2-1紋理合成(Texture Synthesis) 6
2-2影像修補(Image Inpainting) 9
2-3 論文架構 13
第三章研究方法 14
3-1 研究流程 14
3-2 影像幾何校正 17
3-2-1 規則平面紋理資料 17
3-2-2 曲面紋理資料 18
3-3 最適工作視窗大小選取 22
3-3-1 Canny特徵萃取 22
3-3-2 Hough轉換 23
3-4 修補次序計算 26
3-4-1 Isophote等照度面 29
3-4-2 可靠度 30
3-4-3 計算優先權 31
3-5 Image Inpainting 33
3-6 額外參數門檻限定 35
3-6-1 搜尋範圍 37
3-6-2 相似判定 39
3-7 成果評估 41
3-8 特殊案例的牆面紋理處理 44
3-8-1 大規模遮蔽與無法判定結構單元的紋理影像 45
3-8-2 對稱結構的紋理影像 47
第四章成果案例與分析 50
4-1 研究資料簡介 50
4-2 模擬遮蔽案例 52
4-3 規則牆面紋理案例 54
4-3-1 規則牆面紋理案例Ⅰ 54
4-3-2 規則牆面紋理案例Ⅱ 57
4-4 複雜牆面紋理案例 62
4-4-1 複雜牆面紋理案例Ⅰ 62
4-4-2 複雜牆面紋理案例Ⅱ 65
4-5 特殊牆面紋理案例 69
4-5-1 特殊牆面紋理案例Ⅰ 69
4-5-2 特殊牆面紋理案例Ⅱ 73
4-6 群組房屋模型修補成果 80
4-6-1 台北市政府房屋模型牆面紋理修補 80
4-6-2 區域性房屋群組模型牆面紋理修補 86
第五章結論與建議 91
參考文獻 94

參考文獻

林育樂，2004，「應用紋理合成技術於Web3D場景貼圖設計」，碩士論文，國立成功大學工業設計研究所。
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學系。
李唐宇，2007，「結合多元資料重建三維房屋模型」，碩士論文，國立中央大學土木工程學系。
陳正軒，2006，「以視訊影像進行三維房屋模型實景紋理敷貼之研究」，碩士論文，國立中央大學土木工程學系。
Baillard, C., and A. Zisserman, 2000. A plane sweep strategy for the 3D reconstruction of buildings from multiple images, International Archives of Photogrammetry and Remote Sensing, Vol. 33, Part B2, Amsterdam, Netherlands, pp. 56-62.
Bertalmio, M., Sapiro, G., Caselles, V., and Ballester, C., 2000. Image Inpainting, International Conference on Computer Graphics and Interactive Techniques Proceedings of the 27th annual conference on Computer graphics and interactive techniques, Pages 417-424.
Canny, John F., 1986. A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 8:679-698.
Chen, L. -C., C.-Y. Kuo and J.-Y. Rau, 2004. Fusion of LIDAR Data and Large-scale Vector Maps for Building Reconstruction, Proc. 2004 Asian Conference on Remote Sensing, Thailand , No. B-2.6.
Criminisi, A., Perez, P., and Toyama, K.,2004, Region Filling and Object Removal by Exemplar-Based Image Inpainting, IEEE Transactions On Image Processing, Vol. 13, No. 9.
Danahy, J., 1999. Visualization data needs in urban environmental planning and design, Proceedings of the Photogrammetric Week, Karlsruhe, pp.351-365.
Debevec, P. E., 1996. Modeling and Rendering Architecture from Photographs, Ph.D. Dissertation, University of California at Berkeley, CA USA.
Efros, A., and T. Leung, 1999. Texture synthesis by non-parametric sampling, in Proc. Int. Conf. Computer Vision, Kerkyra, Greece, pp. 1033-1038.
Heeger, D., and J. Bergen., 1995. Pyramid based texture analysis/synthesis. Computer Graphics, pp. 229-238, SIGGRAPH 95.
Hough, P.V.C., 1962, Method and means for recognizing complex patterns, U.S.Patent3069654.
Keim, D. A., C. Panse, J. Schneidewind, and M. Sips, 2004. Geo-spatial data viewer: From familiar land-covering to arbitrary distorted geo-spatial quadtree maps, The 12-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, 2 - 6 February, University of West Bohemia, Campus Bory, Plzen, Czech Republic.
Lee, D.S., Shan, J., and Bethel, J.S., 2003. Class-guided building extraction from IKONOS imagery, Photogrammetric Engineering and Remote Sensing, Vol. 69, No. 2, pp. 143-150.
Liang, L., C. Liu, Y. Q. Xu, B. Guo, and H.Y. Shum, 2001. Real-time texture synthesis by patch-based sampling, in ACM Trans. on Graphics, vol. 20, pp. 127-150.
Oliveira, M., M., Bowen, B., McKenna, R., 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.
Portilla, J., and E.P. Simoncelli, 2000. A parametric texture model based on joint statistics of complex wavelet coefficients, Int'1J. Computer Vision, vol. 40, No. 1, pp. 49-71.
Rau, J.Y., and L.C. Chen, 2003. Robust reconstruction of building models from three-dimensional line segments, Photogrammetry Engineering and Remote Sensing, Vol. 69, No. 2, pp. 181-188.
Volz, S., and D. Klinec, 1999. Nexus: The development of a platform for location aware application, Proceedings of the third Turkish-German Joint Geodetic Days, Istanbul, Turkey, Vol. 2, pp.599-608.
Wei, L.Y., and M. Levoy, 2000. Fast texture synthesis using tree-structured vector quantization, in Proc. ACM Conf. Computer Graphics, pp. 479-488.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P., 2004. Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions On Image Processing, Vol. 13, No. 4, pp. 600-612.
Yu, Y., 1999. Efficient visibility processing for projective texture mapping, COMPUTERS & GRAPHICS, 23, pp.245-253

指導教授

蔡富安(Fuan Tsai)

審核日期

2010-1-26

推文