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研究生:蘇法維
研究生(外文):Fa-Wei Su
論文名稱:應用相似性融合演算法於影像分割
論文名稱(外文):Similarity Fusion Algorithm Applied to Image Segmentation
指導教授:王榮華
指導教授(外文):Jung-Hua Wang
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:71
中文關鍵詞:影像分割分水嶺演算法過度分割同質性相似性區塊相鄰圖處理區域統計區塊合併
外文關鍵詞:image segmentationwatershed algorithmover-segmentationhomogeneitysimilarityregion adjacency graph processinglocal statisticsregion merging
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本論文提出一新的方法,稱為相似性融合(SF)演算法來解決常見於watershed-based技術所產生的過度分割(over-segmentation)問題。現今,多數的區塊合併方法是採用逐一合併的方式去減輕這個問題。然而,循序地合併無可避免地造成冗長的運算時間。相反的,SF演算法卻是以同步方式更新區塊的特徵值且使用一個基於同質性區域統計量測方式的動態合併準則來作為一區塊合併的門檻值,此動態準則不但是藉由參考目標區塊的緊鄰區塊而且也參考緊鄰區塊的相鄰區塊。這種兩層式鄰居策略可提供足夠的相鄰區塊資訊來量測相似性程度,藉此大幅提升分割的精確度。此外,由於使用同步更新方式,SF演算法的效能不僅不受初始區塊數多寡的影響,且深具全平行硬體實現的潛力。因為其穩定收歛的特性,相似性融合演算法的另外一個優點就是其自我終止且不需要事先具體指定最終的區塊數量。本論文亦展示大量實驗結果以驗證吾人所提出的方法的運算效率與分割精確度。
This thesis presents a novel approach called Similarity Fusion (SF) algorithm to solve the over-segmentation problem that plagues conventional watershed-based techniques. Currently, most region merging methods merge regions one by one to alleviate the problem in question. However, sequential merging would inevitably incur lengthy computation time. In contrast, SF simultaneously updates features of regions and employs a dynamic thresholding scheme based on a homogeneous measure using local statistics by referring to immediate adjacent regions as well as their neighboring regions. This two-layer adjacency strategy greatly improves the segmentation accuracy by providing sufficient adjacency information for measuring similarity degree. Furthermore, due to the use of synchronous update strategy, SF is not only insensitive to the initial number of regions, but also provides great potentiality for a fully parallel hardware implementation, thereby fast region merging can be achieved. Because of its stable convergence property, SF is self-terminated and needs not pre-specify the final number of regions. Extensive empirical results are provided to verify the computation efficiency and segmentation accuracy of the proposed approach.
CHAPTER 1 INTRODUCTION
1-1. OVERVIEW OF IMAGE SEGMENTATION 2
1-2. MOTIVATION OF THE WORK 7
CHAPTER 2 ALGORITHM OF SIMILARITY FUSION
2-1. MORPHOLOGICAL WATERSHEDS 12
2-2. IMPLEMENTING RAINFALL WATERSHEDS 14
2-3. CHARACTERIZING THE ADJACENT REGIONS 20
2-4. SIMULTANEOUS FEATURE-UPDATING 22
2-5. THE MERGING CRITERION 25
CHAPTER 3 EXPERIMENTS AND CHARACTERIZATIONS
3-1. APPLYING TO IMAGE SEGMENTATION 29
3-2. CHARACTERIZING THE MERGING PROCEDURE 35
3-3. VARIANTS OF MERGING CRITERION 43
3-4. THE l(T) PARAMETER 45
3-5. THE CARDINALITY OF REGIONS 49
3-6. STABILITY ANALYSIS OF THE SF ALGORITHM 51
3-7. WITHOUT THE FEATURE-UPDATING 54
CHAPTER 4 NEURAL NETWORK IMPLEMENTATION
4-1. THE SELF-ORGANIZING FEATURE MAP 57
4-2. IMPLEMENTING SF WITH SOFM 62
CHAPTER 5 CONCLUSIONS AND DISCUSSIONS 65
REFERENCES 67
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