利用可調式區塊比對並結合多圖像資訊之影像運動向量估測

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姓名

王嘉銘(Chia-Ming Wang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

利用可調式區塊比對並結合多圖像資訊之影像運動向量估測
(Multi-frame Motion Estimation Using Adaptive Block Matching)

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摘要(中)

在電腦視覺的領域中，運動估測與偵查是一個有趣而重要的主題。許多的運動估測方法都以計算光流場開始，但是，我們也都了解，傳統的光流估測演算法具有雜訊而且正確性低。
為了獲得更正確的光流估測，我們初步的觀念是認為盡可能利用多張影像資訊。在結合多圖像資訊的光流估測法中，我們採用以相關性為基礎的方法來實作，並且提出兩種不同的方案：一為以參考影像為主的方法，二為遞增法。在實驗當中，我們採用等速度運動的光流模組。對於光流偵測來說，求到子像素的精確度是相當有必要的。我們採用雙線性內插法及三步偵測的搜尋法。最後，我們利用可調式的區塊選擇來做樣板比對。選擇可調式區塊，是採用亮度差值累計法。在實驗結果中，我們可以清楚看到效果的改善。

摘要(英)

Motion estimation and detection is an important and interesting topic in computer vision. Many motion estimation approaches start from the computation of optical flow. However, it is well known that the optical flow vectors estimated with the conventional methods are usually quite noisy and inaccurate.
To obtain more accurate optical flow estimation of an image, our basic idea is to use as much information as possible contained in a number of image frames. In this thesis, we use the correlation-based method. Two different approaches called the reference-frame approach and the incremental approach are used in estimating multi-frame optical flow. We adopt the constant velocity flow model in all of our work. Moreover, sub-pixel refinement is necessary for every estimations. Bilinear interpolation and three-step searching are used in sub-pixel refinement. Finally, we device the adaptive window selection scheme in template matching. The adaptive windows are selected with an efficient algorithm which is called the difference accumulation algorithm. The accuracy improvement is demonstrated in our experimental results.

關鍵字(中)

★ 多圖像
★ 移動偵測
★ 光流偵測
★ 可調式區塊
★ 區塊比對
★ 子像素

關鍵字(英)

★ sub-pixel
★ motion estimation
★ optical flow
★ adaptive window
★ block matching
★ multi-frame

論文目次

CHAPTER 1 INTRODUCTION1
1.1MOTIVATION1
1.2RELATED WORKS3
1.3ORGANIZATION OF THESIS6
CHAPTER 2 MULTI-FRAME OPTICAL FLOW ESTIMATION7
2.1 CORRELATION-BASED METHOD7
2.1.1 Reference-frame Approach8
2.1.2 Incremental Difference Approach9
2.1.3 Comparison9
2.2 DIFFERENT MOTION MODELS11
2.2.1 Constant Velocity Motion Model12
2.2.2 Constant Acceleration Motion Model13
2.2.3 Affine Motion Model14
2.3 SUB-PIXEL BLOCK MATCHING15
2.3.1 Interpolation16
2.3.2 Searching Strategy19
CHAPTER 3 TEMPLATE MATCHING BY ADAPTIVE WINDOW SELECTION22
3.1 ADAPTIVE WINDOW SELECTION USING TOBOGGAN SEGMENTATION24
3.2 ADAPTIVE WINDOW SELECTION USING DIFFERENCE ACCUMULATION METHOD29
3.3 EXPERIMENTAL RESULTS34
CHAPTER 4 EXPERIMENTAL RESULTS40
4.1 EXPERIMENT 1: MULTI-FRAME OPTICAL FLOW ESTIMATION40
4.2 EXPERIMENT 2: DIFFERENT SUB-PIXEL PRECISION LEVEL ESTIMATION47
4.2.1 Error Analysis47
4.2.2 Pixel-Level Estimation Using Synthetic Images of Pixel-Level Motion47
4.2.3 Different Sub-Pixel Level Estimation Using Synthetic Images of Sub-Pixel Level Motion50
4.3 EXPERIMENT 3: ADAPTIVE WINDOW EFFECTS51
CHAPTER 5 CONCLUSIONS AND FUTURE WORKS55
5.1 CONCLUSIONS55
5.2 FUTURE WORKS55
REFERENCES57

參考文獻

References
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指導教授

范國清、洪一平(Kuo-Chin Fan)

審核日期

2000-7-18

推文