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研究生:莊漢笙
研究生(外文):Han-ShengChuang
論文名稱:自動石礫分布萃取基於記號式分水嶺演算法
論文名稱(外文):Automated Coarse-grain Sizing Using Mark-based Watershed Algorithm
指導教授:林昭宏林昭宏引用關係
指導教授(外文):Chao-Hung Lin
學位類別:碩士
校院名稱:國立成功大學
系所名稱:測量及空間資訊學系碩博士班
學門:工程學門
學類:測量工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:51
中文關鍵詞:礫石尺寸粒徑分析數值影像影像分割
外文關鍵詞:Grain SizeGrain-size AnalysisDigital ImageImage Segmentation
相關次數:
  • 被引用被引用:2
  • 點閱點閱:160
  • 評分評分:
  • 下載下載:22
  • 收藏至我的研究室書目清單書目收藏:0
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。
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.
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
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