臺灣博碩士論文加值系統

利用攝影測量產製三維場景或物件乃常規性任務，然而為達致完整及高品質三維重建功效，幾何邊緣特徵對場景或物形的描述最具關鍵性。若能透過影像處理之邊緣偵測獲致完整且精確之幾何邊緣特徵，對於穩固影像密匹配視差不連續處與非視差不連續處有決定性之助益。尤其針對視差不連續處的視差劇烈變化，單一像元的不確定性就容易造成顯著的視差差異，導致前方交會產生嚴重位置偏差，更精確的次像元精度邊緣偵測有其必要性，因此本研究方法利用Edge Drawing搭配動量方程式偵測次像元精度邊緣線後，依據次像元精度邊緣品質使用十字區塊法約制局部式匹配的視差不連續處得到良好視差初值後，再透過改良式的半全域式匹配法優化視差，最後透過左右像檢查與填補以及改良式中值濾波獲取最終精化視差成果。其中本研究先透過視差真值資料萃取真實視差不連續處，來模擬完整及具次像元精度之幾何邊緣，驗證此高品質幾何邊緣資訊確實有助於獲致密匹配於視差連續與不連續區域之真實視差。在實際影像部份，亦針對室內影像及戶外近景與航拍影像進行本演算法之匹配成果分析，依此歸結帶有品質資訊的幾何邊緣對於影像密匹配的助益。此外，本研究提出的品質資訊指標有助於釐清所偵測的影像幾何邊緣是否能有效支援密匹配保留場景幾何邊緣特徵，依據本工作的實驗例所擬定的品質資訊指標門檻能指出弱邊緣特徵區域，達到成果評估功效，據此，亦可導引其他方法加入強化這些區域的匹配成效。

The integrity of geometric edge is vital to the high-quality 3-D scene representation. Thus, under the task of automated as well as successful 3-D scene or object reconstruction by photogrammetric approaches, edge detection becomes an indispensable essential. Various edge detection techniques can only provide the locations of edge up to one pixel accuracy level, which may still incur significant intersection error when dealing with acute depth discontinuities. In this study, Edge Drawing and moment equation are employed to detect subpixel accuracy edges. Then the depth discontinuities based on the subpixel accuracy edges’ quality are applied as constraints in local matching method to initiate a good approximation of disparity, and followed by the modified semi-global matching method to optimize the disparity. The maximum quality gain of subpixel accuracy edge detection aided to dense stereo matching, especially in depth discontinuities, is realized by the image pairs with ground truth disparity. The experimental data consists of indoor images, outdoor close-range images and aerial images. The results of various tests prove that the subpixel accuracy edges with quality indictors can benefit dense stereo matching effectively. The quality indicators of detected edges proposed in this study provide the information about the degree of high quality geometric edges in supporting dense stereo matching around depth discontinuities, thus weakly edge-constrained region can be identified and complementary alternatives can be further integrated to enhance the matching results.

口試委員會審定書 I
誌謝 II
中文摘要 III
英文摘要 IV
圖目錄 VII
表目錄 IX
第一章 前言 1
1.1 研究動機與目的 1
1.2 研究方法與流程 3
1.3 論文架構 5
第二章 文獻回顧 7
2.1 影像密匹配演算法 7
2.1.1 匹配成本值計算 9
2.1.2 匹配成本聚合 10
2.1.3 視差優化 11
2.1.4 視差精化 13
2.2 影像密匹配難題與解決 13
2.3 影像邊緣偵測 15
2.3.1 單一像元精度邊緣偵測 15
2.3.2 次像元精度邊緣偵測 17
第三章 研究方法 20
3.1 EDGE DRAWING 邊緣線偵測 21
3.2 MOMENT-BASED 次像元精度邊緣線偵測 22
3.2.1 一維Moment-based 次像元精度邊緣線偵測 22
3.2.2 觀測量權重 23
3.2.3 擬合理想灰階分布曲線 24
3.3 邊緣品質指標 25
3.4 匹配成本值計算 25
3.5 匹配成本值聚合 26
3.5.1 依據邊緣品質的十字區塊法 27
3.5.2 成本聚合權重 28
3.5.3 初始視差決定 28
3.6 視差優化 29
3.6.1 視差優化範圍 29
3.6.2 SGM懲罰參數決定 30
3.6.3 依據邊緣品質的視差不連續處約制 35
3.7 視差精化 36
3.7.1 左右像一致性檢查 36
3.7.2 缺漏內插 36
3.7.3 改良式的中值濾波 37
3.8 邊緣線品質資訊 38
3.9 真實幾何邊緣萃取 40
3.9.1單一像元精度的真實幾何邊緣 40
3.9.2次像元精度的真實幾何邊緣 41
第四章 實驗分析與討論 43
4.1 實驗一: 真實幾何邊緣對於成果的助益 45
4.1.1 真實次像元幾何邊緣與單一像元幾何邊緣比較 45
4.1.2 真實次像元幾何邊緣於各流程的品質 48
4.2 實驗二: 實際次像元精度邊緣成果比較 50
4.3 實驗三: 邊緣線品質對於成果的助益 53
4.3.1 邊緣品質對於局部式匹配成果影響 53
4.3.2 邊緣品質對於半全域式匹配影響 55
4.4 實驗四: 與其他演算法成果比較 57
4.5 實驗五: 邊緣線品質資訊驗證 60
4.6實驗六: 室外影像成果分析與討論 66
4.6.1 測試區域1 69
4.6.2 測試區域2 74
4.6.3 測試區域3 78
4.6.4 測試區域4 82
4.6.5 實際影像測試小結 86
4.7 實驗七: 弱邊緣線品質區域的成果品質改善 87
4.7.1 實驗室影像 87
4.7.2 航拍影像 91
4.7.3 小結 92
第五章 結論與建議 93
5.1 結論 93
5.2建議 95
參考文獻 96

李欣錡，2016。結合邊緣特徵資訊之半全域式匹配法，國立臺灣大學土木工程學系碩士論文，台北，台灣。
Agustín, T.P., Karl, K., Miguel, A.F., and Daniel, S.C., 2013. Accurate subpixel edge location based on partial area effect. Image and Vision Computing, 31(1), pp. 72-90.
Boykov, Y. and Jolly, M, P., 2001. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. Eighth International Conference on Computer Vision (ICCV 2001), pp. 105–112.
Boykov, Y., Veksler, O., and Zabih, R., 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), pp.1222–1239.
Canny, J., 1986. A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), pp. 679-698.
Chang, Y. J., and Ho, Y. S., 2015. Disparity estimation using edge preserving method. TENCON 2015-2015 IEEE Region 10 Conference, pp.1-4.
Chen, J., Ian, D., Li, .S, Li, Z., Madden, M., Mills, J., Paparoditis, N., Rottensteiner, F., Sester, M., Toth, C., Trinder, J., and Heipke, C., 2016. Information from imagery: ISPRS scientific vision and research agenda. ISPRS Journal of Photogrammetry and Remote Sensing, 115: 3-21.
Chuang, T. Y., Ting, H. W., and Jaw, J. J., 2016. Hybrid-Based Dense Stereo Matching. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLI, Part B3, pp.495-501.
Chuang, T. Y., Ting, H. W., and Jaw, J. J., 2018. Dense Stereo Matching With Edge-Constrained Penalty Tuning. IEEE Geoscience and Remote Sensing Letters, 15(5), pp. 664-668.
Gerrits, M., and Bekaert, P., 2006. Local stereo matching with segmentation-based outlier rejection, The 3rd Canadian Conference on Computer and Robot Vision, pp. 66.
Gu, J., Pan, Y., and Wang, H., 2015. Research on the improvement of image edge detection algorithm based on artificial neural network. Optik-International Journal for Light and Electron Optics, 126(21), pp. 2974-2978.
Gupta, S., Gupta, C., and Chakarvarti, S. K., 2013. Image Edge Detection: A Review. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(2), pp. 2246-2251.
Hagara, M., and Kulla, P., 2011. Edge detection with sub-pixel accuracy based on approximation of edge with Erf function. Radio engineering, 20(2), 516-524.
Hirschmüller, H., Innocent, P. R., and Garibaldi, J., 2002. Real-time correlation-based stereo vision with reduced border errors, International Journal of Computer Vision, 47(1-3), 229-246.
Hirschmüller, H., 2005. Accurate and efficient stereo processing by semiglobal matching and mutual information, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 807–814.
Hirschmüller, H., and Scharstein, D., 2009. Evaluation of stereo matching costs on images with radiometric differences, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9), 1582-1599.
Kolmogorov, V., and Zabih, R., 2001. Computing visual correspondence with occlusions using graph cuts, Proceedings. Eighth IEEE International Conference on Computer Vision 2001, Vol. 2, pp. 508-515.
Loop, C. and Zhang, Z., 1999. Computing rectifying homographies for stereo vision. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 125–131.
Lyvers, E. P., Mitchell, O. R., Akey, M. L., and Reeves, A. P., 1989. Subpixel measurements using a moment-based edge operator. IEEE Transactions on pattern analysis and machine intelligence, 11(12), pp.1293-1309.
Mattoccia, S., 2015. Stereo Vision: Algorithms and Applications, University of Bologna, URL: http://vision.deis.unibo.it/~smatt/Seminars/StereoVision.pdf (last date accessed: 2015)
Maini, R. and Aggarwal, H., 2009. Study and comparison of various image edge detection techniques. International journal of image processing (IJIP), 3(1), pp. 1-11
OpenCV: Canny Edge Detection. Retrieved May 11, 2017, URL: http://docs.opencv.org/trunk/da/d22/tutorial_py_canny.html (last date accessed: 2018)
Qu, Y.D., Cui, C.S., Chen, S.B., and Li, J.Q., 2005. A fast subpixel edge detection method using Sobel–Zernike moments operator. Image and Vision Computing, 23(1):pp.11-17.
Rothermel, M., Wenzel, K., Fritsch, D., and Haala, N., 2012. SURE: Photogrammetric surface reconstruction from imagery, Proc. LC3D Workshop, p. 29.
Scharstein, D., and Szeliski, R., 2002. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International journal of computer vision, 47(1-3), 7-42.
Scharstein, D., and Szeliski, R., 2003. High-accuracy stereo depth maps using structured light, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 195-202.
Scharstein, D., and Szeliski, R., 2008. Middlebury Stereo Vision Page. http://vision.middlebury.edu/stereo/ (17 Apr. 2018)
Sun, J., Zheng, N. N., and Shum, H. Y., 2003. Stereo matching using belief propagation. IEEE Transactions on pattern analysis and machine intelligence, 25(7), 787-800.
Szeliski, R. and Golland, P., 1999. Stereo matching with transparency and matting. International Journal of Computer Vision, 32(1), 45–61.
Szeliski, R., 2010. Computer vision: algorithms and applications. Springer Science & Business Media, London, London.
Tabatabai, A. J., and Mitchell, O. R., 1984. Edge location to subpixel values in digital imagery. IEEE transactions on pattern analysis and machine intelligence, (2), pp.188-201.
Topal, C., and Akinlar, C., 2012. Edge Drawing: A combined real-time edge and segment detector. Journal of Visual Communication and Image Representation, 23(6), pp. 862-872.
Tian, Z., C. Xu, and X. Huang, 2013. Real-Time Stereo Matching using Adaptive Window base Disparity Refinement, Proceedings of the International Conference on Mulimedia and Human Computer Interaction, Ontario, Canada, No. XXX.
Yoon, K. J., and Kweon, I. S., 2006. Adaptive support-weight approach for correspondence search, IEEE Transactions on Pattern Analysis & Machine Intelligence, (4), 650-656.
Wei, W., and Ngan, K. N., 2005. Disparity estimation with edge-based matching and interpolation. International Symposium on Intelligent Signal Processing and Communication Systems, pp. 153-156.
Zhan, Y., Gu, Y., Huang, K., Zhang, C., and Hu, K., 2015. Accurate Image-guided Stereo 59 Matching with Efficient Matching Cost and Disparity Refinement, IEEE Transactions on Circuits and Systems for Video Technology, PP (99).
Zhang, K., Lu, J., and Lafruit, G., 2009. Cross-based local stereo matching using orthogonal integral images, IEEE Transactions on Circuits and Systems for Video Technology, 19(7), 1073-1079.