進階搜尋


下載電子全文  
系統識別號 U0026-2108201515063500
論文名稱(中文) 最佳鑲嵌線研究應用於航空影像拼接
論文名稱(英文) A Study of Optimal Seamline Determination for Aerial Image Mosaicking
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 103
學期 2
出版年 104
研究生(中文) 陳柏亨
研究生(英文) Bo-Heng Chen
學號 P66021033
學位類別 碩士
語文別 英文
論文頁數 47頁
口試委員 指導教授-林昭宏
口試委員-蔡榮得
口試委員-黃怡碩
中文關鍵字 影像鑲嵌  影像金字塔  最佳路徑偵測 
英文關鍵字 image mosaicking  image pyramid  optimal path determination 
學科別分類
中文摘要 影像拼接是產製全幅正射影像重要的一環,其中鑲嵌線的位置將決定拼接成果的品質。為了無縫接合各個影像區塊,過往的研究提出數個找尋最佳鑲嵌線的方法,這些方法多採用戴克斯特拉演算法(Dijkstra’s algorithm),在影像重疊區進行鑲嵌線的偵測。但戴克斯特拉法對於路徑長度的約制,將限縮可能的鑲嵌線路徑並使其通過影像間高差異的區域,在拼接影像中造成明顯的鑲嵌痕跡。為了改善這個問題,過往研究的方法可分成訂定適當閥值以禁止鑲嵌線通過高差異獨立地物,以及限制鑲嵌線只通過特定向量路徑等兩類。但在各影像組中的最佳閥值會隨情況差異有所不同,且收集和套用向量路徑資料需要複雜的前處理步驟。因此本研究提出一個直接對目標函式和求解方法上進行調整的新路徑尋找法,以最小化路徑平均差異的方式避免鑲嵌線通過高差異區域。考量到拼接作業的處理時間,本研究將套用既有的階層式架構方法提升執行效率,並投影產生多像元寬的鑲嵌帶以平滑化影像交界。透過本研究所提出的拼接策略,由於鑲嵌線通過獨立地物所產生的不連續及鑲嵌痕跡將被改善。在不同實驗組影像上進行的定量和定性分析,顯示本研究的方法能夠找尋到近似最佳鑲嵌線的路徑成果。
英文摘要 Image mosaicking is a fundamental step in composing of a set of aerial orthoimages. Determining the optimal seamline is thus important in seamlessly merging the image patches. Numerous seamline searching approaches have been proposed to solve this optimization problem in previous studies. Most of these studies apply Dijkstra’s algorithm (DA) to identify a seam under the objective function of the shortest path. However, seamlines identified using this strategy may pass high-cost regions, potentially resulting in seam artifacts. To address this problem, previous studies set a threshold in filtering out high-cost pixels prior to determining the seamline or constrain the seamline to pass certain vector paths. Nonetheless, proper threshold values are case sensitive, and information of vector graphics requires non-trivial processes of data collection and fusion. This study proposes a novel method with several extensions on both the objective function of the seamline optimization and the optimization solver. Without thresholding and other geographic data, the proposed method avoids seamlines that pass high-cost regions by searching for a seam under the objective function of a path with minimal average mismatch. Considering the color transition and computational cost of image mosaicking, a hierarchical structure is also applied to the seamline determination in our study. Determining a seamline in a specific level of the image pyramid reduces computational cost and also generates a seam zone that softens color transition. Thus, applying these strategies can decrease the occurrence of seam artifacts caused by the discontinuity of high-cost regions. Qualitative and quantitative analyses of various image sets indicate that the proposed method can determine near-optimal seamlines.
論文目次 摘要 I
Abstract II
致謝 III
List of Table V
List of Figure V
Chapter 1 Introduction 1
Chapter 2 Background 7
2.1 Review of DP 7
2.2 Review of the Image Pyramid 8
Chapter 3 Methodology 10
3.1 System Workflow 10
3.2 Image Preprocessing 11
3.2.1 Output Model Construction 11
3.2.2 Color Balancing 13
3.3 Cost Map Generation 14
3.4 Path Finding Algorithm 16
3.4.1 DA 17
3.4.2 Proposed Method 19
3.5 Seamline Projection 21
Chapter 4 Experimental Results and Discussions 24
4.1 Study Areas 24
4.2 Experimental Results 25
4.2.1 Comparison of the Path-finding Methods 26
4.2.2 Comparison of the Seam Zones 35
4.2.3 Comparison of Related Methods 40
Chapter 5 Conclusions and Future Work 43
5.1 Conclusions 43
5.2 Future Work 44
References 45
參考文獻 Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M., “Pyramid method in image processing,” RCA Engineer, vol. 29, no. 6, pp. 33-41, 1984.
Burt, P.J., Adelson, E.H., “A multiresolution spline with application to image mosaics”, ACM Transactions on Graphics, vol. 2, no. 4, pp. 217-236, 1983.
Brown, M., Lowe, D.G., “Automatic Panoramic Image Stitching using Invariant Features,” International Journal of Computer Vision, vol. 74, no. 1, pp. 59-73, 2007.
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V., “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, pp. 346–359, 2008.
Botterill, T., Mills, S., Green, R., “Real-time aerial image mosaicking”, 25th International Conference of Image and Vision Computing New Zealand, pp. 1-8, 2010.
Chon, J., Kim, H., Lin, C.S., “Seam-line determination for image mosaicking: A technique minimizing the maximum local mismatch and the global cost,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, no. 1, pp. 86-92, 2010.
Chou, H. S., Chen, J.Y., Lin, C.H., “Automatic Determination of Blending Zone for Aerial Image Mosaicking,” master thesis, Department of Geomatics, National Cheng-Kung University, 2013.
Chen, Q, Sun, M., Hu, X., “Automatic Seamline Network Generation for Urban Orthophoto Mosaicking with the Use of a Digital Surface Model,” Remote Sensing, vol. 6, no. 12, pp. 12334-12359, 2014.
Dijkstra, E.W., “A note on two problems in connexion with graphs,” Numerische
Mathematik, vol. 1, pp. 269–271, 1959.
Eddy, S.R., “What is dynamic programming,” Nature Biotechnology, vol. 22, no. 7, pp. 909-910, 2004.
Fernandez, E., Garfinkel, R., Arbiol, R., “Mosaicking of aerial photographic maps via seams defined by bottleneck shortest paths,” Operations Research, vol. 46, no. 3, pp. 293-304, 1998.
Kerschner, M., “Seamline detection in colour orthoimage mosaicking by use of twin snakes,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 56, no. 1, pp. 53-64, 2001.
Mills, S., McLeod, P., “Global seamline networks for orthomosaic generation via local search,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 75, pp. 101-111, 2013.
Oliveira, M., Sappa, A.D., Santos, V., “A Probabilistic Approach for Color Correction in Image Mosaicking Applications,” IEEE Transactions on Image Processing, vol. 24, no. 2, pp. 508-23, 2015.
Pan, J., Wang, M., Li, D., Li, J., “Automatic generation of seamline network using area Voronoi diagrams with overlap,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 6, pp. 1737-1744, 2009.
Pan, J., Wang, M., “A seam-line optimized method based on difference image and gradient image,” IEEE 19th International Conference on Geoinformatics, pp. 1-6, 2011.
Pan, J., Zhou, Q., Wang, M., “Seamline Determination Based on Segmentation for Urban Image Mosaicking,” IEEE Geoscience And Remote Sensing Letters, vol. 11, no. 8, pp. 1335-1339, 2014.
Pan, J., Wang, M., Li, D., Zhou, Q., Li, J., “Seamline Network Refinement Based on Area Voronoi Diagrams With Overlap,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 3, pp. 1658-66, 2014.
Sniedovich, M., “Dijkstra’s algorithm revisited: the dynamic programming connexion”, Control and Cybernetics, vol. 3, no. 3, 2006.
Uyttendaele, M., Eden, A., Szeliski, R., “Eliminating ghosting and exposure artifacts in image mosaics,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 509-516, 2001.
Wan, Y., Wang, D., Xiao, J., Wang, X., Yu, Y., Xu, J., “Tracking of vector roads for the determination of seams in aerial image mosaics,” IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 3, pp. 328-332, 2012.
Wan, Y., Wang, D., Xiao, J., Lai, X., Xu, J., “Automatic determination of seamlines for aerial image mosaicking based on vector roads alone,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 76, pp. 1-10, 2013.
Yu, L., Holden, E.J., Dentith, M.C., , Zhang, H., “Towards the automatic selection of optimal seam line locations when merging optical remote-sensing images,” International Journal of Remote Sensing, vol. 33, no. 4, pp. 1000-1014, 2012.
Zhong, C., Yang, Y., “Orthoimage seamline searching based on minimizing local maximum algorithm,” Proceedings of the SPIE - The International Society for Optical Engineering, Vol. 8203, 82030H (6 pp.), 2010.

論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2015-08-31起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2015-08-31起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw