請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71943
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 劉格非 | |
dc.contributor.author | Jia-Wei Liou | en |
dc.contributor.author | 劉家瑋 | zh_TW |
dc.date.accessioned | 2021-06-17T06:15:57Z | - |
dc.date.available | 2020-02-13 | |
dc.date.copyright | 2019-02-13 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-09 | |
dc.identifier.citation | Beucher, S., & Meyer, F. (1992). The morphological approach to segmentation: the watershed transformation. Optical Engineering-New York-Marcel Dekker Incorporated-, 34, 433-433.
Borgefors, G. (1986). Distance transformations in digital images. Computer vision, graphics, and image processing, 34(3), 344-371. Bunte, K., & Abt, S. R. (2001). Sampling surface and subsurface particle-size distributions in wadable gravel-and cobble-bed streams for analyses in sediment transport, hydraulics, and streambed monitoring. Gen. Tech. Rep. RMRS-GTR-74. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. 428 p., 74. Butler, J. B., Lane, S. N., & Chandler, J. H. (2001). Automated extraction of grain-size data from gravel surfaces using digital image processing. Journal of hydraulic Research, 39(5), 519-529. Chang, F.-J., & Chung, C.-H. (2012). Estimation of riverbed grain-size distribution using image-processing techniques. Journal of Hydrology, 440, 102-112. Chung, C.-H., & Chang, F.-J. (2013). A refined automated grain sizing method for estimating river-bed grain size distribution of digital images. Journal of Hydrology, 486, 224-233. Cislaghi, A., Chiaradia, E. A., & Bischetti, G. B. (2016). A comparison between different methods for determining grain distribution in coarse channel beds. International Journal of Sediment Research, 31(2), 97-109. Dietrich, W. E. (1982). Settling velocity of natural particles. Water Resources Research, 18(6), 1615-1626. Diplas, P., & Fripp, J. B. (1992). Properties of various sediment sampling procedures. Journal of Hydraulic Engineering, 118(7), 955-970. Diplas, P., & Sutherland, A. J. (1988). Sampling techniques for gravel sized sediments. Journal of Hydraulic Engineering, 114(5), 484-501. Ferguson, R., & Church, M. (2004). A simple universal equation for grain settling velocity. Journal of Sedimentary Research, 74(6), 933-937. Fitzgibbon, A., Pilu, M., & Fisher, R. B. (1999). Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5), 476-480. Fitzgibbon, A. W., & Fisher, R. B. (1996). A buyer's guide to conic fitting. DAI Research paper. Folk, R. L. (1966). A review of grain‐size parameters. Sedimentology, 6(2), 73-93. Folk, R. L., & Ward, W. C. (1957). Brazos River bar: a study in the significance of grain size parameters. Journal of Sedimentary Research, 27(1), 3-26. Gonzalez, R., & Wintz, P. (1977). Digital image processing. Graham, D. J., Reid, I., & Rice, S. P. (2005). Automated sizing of coarse-grained sediments: image-processing procedures. Mathematical Geology, 37(1), 1-28. Harb, G., & Schneider, J. (2009). Application of two automated grain sizing approaches and comparison with traditional methods. Paper presented at the Proceedings of the 33rd IAHR Congress: Water Engineering for a Sustainable Environment. Interna-tional Association of Hydraulic Engineering & Research (IAHR). Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision: Cambridge university press. Hey, R. D., & Thorne, C. R. (1983). Accuracy of surface samples from gravel bed material. Journal of Hydraulic Engineering, 109(6), 842-851. Horn, B. K. (2000). Tsai’s camera calibration method revisited. Online: http://people. csail. mit. edu/bkph/articles/Tsai_Revisited. pdf. Ibbeken, H., & Schleyer, R. (1986). Photo‐sieving: A method for grain‐size analysis of coarse‐grained, unconsolidated bedding surfaces. Earth Surface Processes and Landforms, 11(1), 59-77. Kellerhals, R., & Bray, D. I. (1971). Sampling procedures for coarse fluvial sediments. Journal of the Hydraulics Division, 97(8), 1165-1180. Kittler, J., & Illingworth, J. (1986). Minimum error thresholding. Pattern recognition, 19(1), 41-47. Kuhnle, R., Wren, D., Langendoen, E., & Rigby, J. (2012). Sand transport over an immobile gravel substrate. Journal of Hydraulic Engineering, 139(2), 167-176. Leopold, L. B. (1970). An improved method for size distribution of stream bed gravel. Water Resources Research, 6(5), 1357-1366. McEwan, I., Sheen, T., Cunningham, G., & Allen, A. (2000). Estimating the size composition of sediment surfaces through image analysis. Paper presented at the Proceedings of the Institution of Civil Engineers-Water and Maritime Engineering. Meyer-Peter, E., & Müller, R. (1948). Formulas for bed-load transport. Paper presented at the IAHSR 2nd meeting, Stockholm, appendix 2. Meyer, F. (1994). Topographic distance and watershed lines. Signal processing, 38(1), 113-125. Montgomery, D. R., & Buffington, J. M. (1993). Channel classification, prediction of channel response, and assessment of channel condition: University of Washington Seattle. Montgomery, D. R., & Buffington, J. M. (1997). Channel-reach morphology in mountain drainage basins. Geological Society of America Bulletin, 109(5), 596-611. Montgomery, D. R., & Buffington, J. M. (1998). Channel processes, classification, and response. River Ecology and Management: Lessons from the Pacific Coastal Ecoregion, RJ Naiman and RE Bilby (Editors). Springer-Verlag, New York, New York, 13-42. Nakagawa, Y., & Rosenfeld, A. (1979). Some experiments on variable thresholding. Pattern recognition, 11(3), 191-204. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66. Pierre, D. A. (1969). Optimization theory with applications: Courier Corporation. Rosgen, D. L. (1994). A classification of natural rivers. Catena, 22(3), 169-199. Rosgen, D. L. (1996). Applied river morphology: Wildland Hydrology. Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic imaging, 13(1), 146-166. Shields, A. (1936). Anwendung der Aehnlichkeitsmechanik und der Turbulenzforschung auf die Geschiebebewegung. PhD Thesis Technical University Berlin. Sime, L., & Ferguson, R. (2003). Information on grain sizes in gravel-bed rivers by automated image analysis. Journal of Sedimentary Research, 73(4), 630-636. Strom, K., Kuhns, R., & Lucas, H. (2010). Comparison of automated image-based grain sizing to standard pebble-count methods. Journal of Hydraulic Engineering, 136(8), 461-473. Tsai, R. (1987). A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal on Robotics and Automation, 3(4), 323-344. Vincent, L. (1993). Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE transactions on image processing, 2(2), 176-201. Wolman, M. G. (1954). A method of sampling coarse river‐bed material. EOS, Transactions American Geophysical Union, 35(6), 951-956. Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334. 莊漢笙. (2012). 自動石礫分布萃取基於記號式分水嶺演算法. 陳嘉欣, 邵允銓, 王驥魁, & 吳富春. (2008). 河床質粒徑分布之數位影像光篩分析. 農業工程學報, 54(4), 16-32. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71943 | - |
dc.description.abstract | 河床剪應力(bed shear stress)、粒子沉降速度(grain settling velocity)、河床運移(bed load transport)等計算公式皆以粒徑(grain size)作為參數。而傳統求取河床表面粒徑分佈主要依靠人工進行採樣並量測,此方法雖然較精確且適用於各種環境,但需要耗費大量的人力及時間成本。近期隨著數位相機的普及與蓬勃發展,許多研究開始朝向以影像分析取代人工量測的自動化粒徑分析(Automated Grain Sizing, AGS)技術。縱使自動化粒徑分析技術於室內實驗中已得到很好的驗證;然而對於現地環境亮度分佈不均勻、石頭表面雜訊過多等因素,皆使得此技術離全自動監測的目標還有許多需要克服的地方。
本研究改進了自動化粒徑分析的架構,並將分析流程分為四大步驟。首先,從影像修正開始將影像修正為正射影像並進行環境亮度的修正,接著分別透過基於面與基於線的影像萃取方法萃取出影像中的石頭本體、邊界與背景區域,再將兩種萃取方法進行結合與修正;然而此時影像中部份區域的石頭仍然相互連通,故藉由標記分水嶺法對二值影像進行強制切割,使影像辨識的石頭數量與人眼辨識的結果達到相同數量級,最後利用橢圓擬合的方式進行粒徑量測,並透過分析與驗證可知道本研究分析一顆石頭長度的基本分析誤差為±10個像素寬。此外,本研究利用影像中的最大粒徑,取代分析程式中五項參數的設定,使分析流程達成半自動分析的目標。 最後本研究於三種不同的現地環境中,將方形網格採樣(Grid Sampling)之結果與本研究結果相比可發現,各粒徑累積百分比所對應的粒徑長度誤差皆小於預期的±10個像素寬以內。而本研究平均計算一張兩千四百萬畫素的影像所需花費的時間為12秒,相對於人工採樣量測的方法來說減少了約300倍的分析時間。 | zh_TW |
dc.description.abstract | Grain-size distributions (GSDs) often be using as a parameter in bed shear stress, grain settling velocity, bed load transport, etc. The traditional measurement of GSDs mainly relies on manual sampling and measurement. Although this method is more accurate and suitable for various environments, it requires a lot of manpower and time cost. Recently, with the popularity and flourishing of digital cameras, many studies have begun to develop automated grain sizing (AGS) based on image analysis. Even though AGS has performed well in indoor experiments, the unevenness light effect and grains with excessive noise in outdoor environment make the technology still have many problems to be overcome.
This study improved the architecture of AGS and divided the analysis process into four major steps. First, rectify the image to an orthophoto and adjust the unevenness light. Second, the image segmentation including line-based extraction and area-based extraction were used. Then we integrated these two extraction results to increase the degree of extraction and extracted a binary image. With this binary image, each grain boundary were separated by the well-known watershed segmentation method. Finally, the representative grain-sizes value were calculated after ellipse fitting. Through analysis and verification, we know that the basic analysis error of a grain size in this study is ±10 pixels. In addition, this study uses the maximum grain size in the image to replace the five parameters in the analysis program, so that the analysis process achieves the goal of semi-automatic analysis. The results of grid sampling were compared with the results of this study in three different outdoor environments. The error corresponding to the cumulative percentage of each grain size was less than ±10 pixels. In this study, the average calculation time for a picture of 24 million pixels is 12 seconds, which is about 300 times less than the manual sampling method. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:15:57Z (GMT). No. of bitstreams: 1 ntu-107-R05521301-1.pdf: 27453693 bytes, checksum: 782f7c9fe392d60ad5d7a694033c27ca (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 致謝 I
摘要 II ABSTRACT III 目錄 V 圖目錄 VII 表目錄 IX 第一章 緒論 1 1.1 研究背景與目的 1 1.2 論文架構 2 第二章 文獻回顧 3 第三章 分析方法 5 3.0 基礎定義 5 3.0.1像素之鄰域 5 3.0.2像素之連通 5 3.0.3影像形態學運算 6 3.1 分析架構 9 3.2 影像修正 12 3.2.1影像灰階化 12 3.2.2透視投影轉換 13 3.2.3消除環境亮度 13 3.3 影像萃取 22 3.3.1影像自動二值化(基於面的萃取) 22 3.3.2計算影像梯度 29 3.3.3梯度幅值二值化(基於線的萃取) 29 3.3.4結合萃取結果 32 3.3.5修正結合結果 34 3.4 影像分割 36 3.4.1求取區域標記 36 3.4.2分水嶺演算法切割 39 3.5 粒徑量測 42 3.5.1刪除邊界顆粒 42 3.5.2橢圓擬合 43 第四章 實驗驗證 53 4.1 實驗配置 53 4.2 實驗結果 56 4.3 結果分析 63 第五章 結論與建議 67 5.1 結論 67 5.2 建議 68 參考文獻 69 附錄 73 | |
dc.language.iso | zh-TW | |
dc.title | 影像分析應用於河床表面粒徑分佈計算 | zh_TW |
dc.title | Image Analysis Applied to the Calculation of Grain Size Distribution on Riverbed Surface | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 詹錢登,周憲德 | |
dc.subject.keyword | 粒徑分佈,影像分析,影像分割,自動化粒徑分析, | zh_TW |
dc.subject.keyword | grain-sizes distribution,image processing,image segmentation,automated grain sizing, | en |
dc.relation.page | 74 | |
dc.identifier.doi | 10.6342/NTU201802515 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-09 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-107-1.pdf 目前未授權公開取用 | 26.81 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。