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系統識別號 U0026-0509201209544900
論文名稱(中文) 自動石礫分布萃取基於記號式分水嶺演算法
論文名稱(英文) Automated Coarse-grain Sizing Using Mark-based Watershed Algorithm
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系碩博士班
系所名稱(英) Department of Geomatics
學年度 100
學期 2
出版年 101
研究生(中文) 莊漢笙
研究生(英文) Han-Sheng Chuang
學號 P66991076
學位類別 碩士
語文別 英文
論文頁數 51頁
口試委員 指導教授-林昭宏
共同指導教授-王驥魁
口試委員-蔡富安
口試委員-黃倬英
中文關鍵字 礫石尺寸  粒徑分析  數值影像  影像分割 
英文關鍵字 Grain Size  Grain-size Analysis  Digital Image  Image Segmentation 
學科別分類
中文摘要 粒徑分析主要目的為量測一河床區域礫石尺寸分布,礫石分布對於河床形貌與棲地有極大影響。傳統調查粒徑分析方式費時耗力,而使用數位相機拍照取樣分析粒徑可大幅減少人力及時間成本。因此,自動影像處理在數值粒徑分析中扮演不可或缺的腳色。本研究提出一可靠且精確的自動影像處理流程萃取河床礫石影像。其基本概念為分階處理,萃取出概略區域後再做一細微分割,分為粗分割及細分割部分。於粗分割階段,利用線段偵測以及線段連接演算法分別萃取出概略的礫石及非礫石陰影區域。此些區域,可作為細分割步驟中分水嶺演算法的記號區,以得到完整的分割。於細分割步驟中,使用記號式分水嶺演算法取代傳統分水嶺演算法,可避免礫石嚴重過度分割外亦可將其他資訊做為演算法的參考。除此之外,利用分割後礫石區域邊界的線段的平滑度作為合併的條件,以降低過度分割的問題。並且提供可調參數架構克服各式不同環境下的影像。最後以多樣性影像進行測試,結果顯示本研究所提出方式可得到可靠的結果。
英文摘要 Grain sizing is a process of measuring surface grain-size distributions (GSDs) from a sample of sediment. Measuring GSDs using digital images has been proven that is much more efficient than traditional manual field methods such as sieving and settling. Thus, automatic image analysis plays an important role in the GSDs determination. This study proposes a novel method to accurately, automatically, and efficiently extract information of grain sizes from digital sediment images. Based on the idea of coarse-to-fine segmentation, we propose a mark-based watershed algorithm that extracts grains in two stages: coarse segmentation and fine segmentation. In the stage of coarse segmentation, the rough locations of grains and interstices between grains are determined by edge detection and proposed edge linking techniques. The previous detections are regarded as marks and used to further refine the partition results in the fine segmentation stage using proposed mark-based watershed algorithm. In this study, instead of selecting pixels with local minima as marks, we select markers with prior knowledge. This manner enables our method to significantly ease the problem of over-segmentation occurred in traditional segmentation algorithms, and to extract grains accurately. In addition, a criterion of smoothness of grain boundary, i.e., a shape descriptor, is proposed to further solve the problem of over-segmentation in post-processing. Besides, a tunable scheme of only three parameters is provided with an interactive grain sizing system to ease the difficulties caused by various image acquisition conditions such as sensors, lighting, shadows, and various grain conditions such as grain shapes and textures. Qualitative and quantitative analyses on images containing various sediments are conducted to evaluate the proposed method. The experimental results show that the proposed method can yield better results, in terms of accuracy of GSDs measurement, compared to related image-processing-based method.
論文目次 Catalog
摘要 I
Abstract II
致謝 IV
Catalog V
List of Table VII
List of Figure VIII
Chapter 1 INTRODUTION 1
Chapter 2 RELATED WORK 3
Chapter 3 METHODOLOGY 7
3.1. Pre-processing 9
3.2. Image Processing 11
3.2.1. Couse Detection 13
3.2.2. Fine Detection 22
3.3. Grain-size Distribution 26
Chapter 4 Experimental Results and Analysis 28
4.1. Parameter Setting 29
4.2. Results 31
4.3. Performance 37
4.4. Comparison 40
Chapter 5 CONCLUSIONS 46
References 48
參考文獻 Bader, H., Hyperbolic distribution of particle sizes, Journal of Geophysical Research, vol. 75, pp. 2822–2830, 1970.
Beucher, S., The Watershed Transformation Applied To Image Segmentation, Scanning Microscopy International, vol. 6, pp. 299–314, 1992.
Butler, J. B., Lane, S. N., and Chandler, J. H., Automated extraction of grain-size data from gravel surfaces using digital image processing, Journal of Hydraulic Research, vol. 39, pp. 519–529, 2001.
Buscombe, D., and Masselink, G., Grain size information from the statistical properties of digital images of sediment, Sedimentology, vol. 56, pp. 421–438, 2009.
Barnard, P. L., Rubin, D. M., Harney, J., and Mustain, N., Field test comparison of an autocorrelation technique for determining grain size using a digital “beachball” camera versus traditional methods, Sedimentary Geology, vol. 201, pp. 180-195, 2007.
Buscombe, D., Rubin, D. M., and Warrick, J. A., A universal approximation of grain size from images of noncohesive sediment, Journal of Geophysical Research-Earth Surface, vol. 115, 2010.
Buscombe, D., Estimation of grain‐size distributions and associated parameters from digital images of sediment, Sedimentary Geology, vol. 210, pp. 1–10, 2008.
Carbonneau, P. E., The threshold effect of image resolution on image‐based automated grain size mapping in fluvial environments, Earth Surface Processes and Landforms, vol. 30, pp. 1687–1693, 2005.
Carbonneau, P.E., Bergeron, N.E., and Lane, S.N., Automated grain size measurements from airborne remote sensing for long profile measurements of fluvial grain sizes, Water Resources Research, vol. 41, pp. 11, 2005.
Carbonneau, P. E., Lane, S. N., and Bergeron, N., Catchment scale mapping of surface grain size in gravel bed rivers using airborne digital imagery, Water Resources Research, vol. 40, 2004.
Canny, J.,A Computational Approach To Edge Detection, IEEE Pattern Analysis and Machine Intelligence, vol. 8(6):pp.679–698, 1986
Dugdale, S.J., Carbonneau, P.E. and Campbell, D., Aerial photosieving of exposed gravel bars for the rapid calibration of airborne grain size maps, Earth Surface Processes and Landforms, vol.35(6), pp. 627-639, 2010.
Frank, Y. Shih, Shouxian Cheng, Adaptive Mathematical Morphology For Edge Linking, Information Sciences, vol. 167, pp. 9-21,2004.
Gale, S.J. and Hoare, P.G. Bulk sampling and coarse clastic sediments for particle size analysis, Earth Surface Processes and Landforms, vol. 17, pp.729–734, 1992
Graham, D. J., Rice, S. P., and Reid I., A transferable method for the automated grain sizing of river gravels, Water Resources Research, vol. 41, 2005.
Luc Vincent, Pierre Solle, Watersheds in Digital Space: An Efficient Algorithm Based on Immersion, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 13, no. 6, 1991.
McEwan, I. K., Sheen, T. M., Cunningham, G. J., and Allen, A. R., Estimating the size composition of sediment surfaces through image analysis, Proc. Inst. Civ. Eng. Water Mar. Energy, vol. 142, pp. 189– 195, 2000.
Otsu N., A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979.
Pentney, R., Dickson, M.E., Digital grain size analysis of a mixed sand and gravel beach, Journal of Coastal Research, vol. 28(1), pp. 196-201, 2012.
Rubin, D. M., Chezar, H., Harney, J.N., Topping, D.J., Melis, T.S., and Sherwood, C.R., Underwater microscope for measuring spatial and temporal changes in bed-sediment grain-size. Sedimentary Geology, vol. 202, pp. 402–408, 2007.
Rubin, D.M., A simple autocorrelation algorithm for determining grain size from digital images of sediment, Journal of Sedimentary Research, vol. 74, pp. 160-165, 2004.
Sime, L. C. and Ferguson, R. I., Information on grain sizes in gravel-bed rivers by automated image analysis, Journal of Sedimentary Research, vol. 73, pp. 630– 636, 2003.
Warrick, J.A., Rubin, D.M., Ruggiero, P., Harney, J.N., Draut, A.E., and Buscombe, D., Cobble cam: grain size measurements of sand to boulder from digital photographs and autocorrelation analyses, Earth Surface Processes and Landforms, vol. 34(13), pp. 1811–1821, 2009.
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