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研究生:黃子魁
研究生(外文):Tz-kuei Huang
論文名稱:用多重解析度為基準來分離出單張影像中的陰影/反光
論文名稱(外文):Multi-Resolution-Based Shading/ Reflectance and Specularity/Diffusion Separation Using a Single Image
指導教授:連震杰
指導教授(外文):Jenn-Jier Lien
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2008
畢業學年度:96
語文別:英文
論文頁數:43
中文關鍵詞:陰影多重解析反光
外文關鍵詞:shadingspecularmulti-resolution
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反光及陰影都是光線變化所造成的,然而這會影響到許多電腦視覺及影像處理的演算法,例如影像分割、物體追蹤、識別、偵測等。因此,發展一套可以將陰影/反光去除的系統是有其必要的。這本篇論文裡,我們將使用多重解析度為基準來分離出單張影像中的陰影/反光。首先我們利用圖形模型來分離出陰影,與先前提出的方法不同的是,訓練資料為不同地點取得的影像序列,並將其分割成小塊的影像,然後用Markov網絡找出之間的關係。將測試資料套用我們訓練出來的關係,即可獲得我們所要的結果。在論文的第二部份,我們先使用一個完全只利用顏色特性的方法去偵測出反光的區域,接著利用多重解析度的特性將反光的部分分離出來。首先把影像降維度,直到影像中沒有反光成分的存在。在做升維度的時候,非反光的區域將被保留,而反光區域由較低解析度的圖更新。我們利用求最小平方誤差解這個方法來修正升維度的結果,最後取得一張沒有反光的影像。
Both shading and specular are caused by lighting, and produce troublesome effects for algorithms of computer vision and image processing, such as segmentation, object tracking, recognition, detection, etc. Therefore, develop a system to separate them out is required. In this paper, we use multi-resolution based methods to solve shading/specular problem using a single image. Firstly, we discuss a method to separate shading and reflectance with graphical mode. Unlike previous methods of estimating shading images, our training data are generated from image sequences shot from different scenes and lightings. Training sets are generated by pairing original image patches with their illumination image patches at different scales, and then their relationships are modeled by Markov network. During testing, belief propagation is utilized to search for illumination patches that best described the input image patches by both considering shading consistency and minimizing the total amount of edge derivates. In the second part, we detect specular by a method solely on colors. Then we do down-sampling until there is no any specular components exists. When up-sampling, we update the specular pixels by lower resolution and keep diffuse pixels. Finally, least square solution is used for correcting our results.
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 THE SHADING AND REFLECTANCE SEPARATION 3
2.1 RELATED WORK 3
2.1.1 Maximum Likelihood (ML) Approach 3
2.1.2 Chorminance Based Approach 4
2.1.3 Learning Based Approach 4
2.2 SYSTEM FLOWCHART 5
2.3 TRAINING PROCESS 7
2.3.1 Training Data Collection 7
2.3.2 Patch-Based Multi-resolution Approach 8
2.3.3 Graphical Model 9
2.4 TESTING PROCESS 11
2.5 EXPERIMENTAL RESULTS 14
2.5.1 Test Data Collection 14
2.5.2 Test Results 14
CHAPTER 3 THE SPECULARITY AND DIFFUSION SEPARATION 17
3.1 RELATED WORK 17
3.1.1 Reflection Model 18
3.2 SYSTEM FLOWCHART 21
3.3 SPECULAR-PIXEL DETECTION METHOD 21
3.3.1 Intensity-Chromaticity Domain 23
3.3.2 Specular-to-Diffuse Mechanism 23
3.3.3 Intensity Logarithmic Differentiation 26
3.3.4 Color Boundary 27
3.3.5 Iterative Framework 28
3.3.6 Problem 30
3.3.7 Detection Results 30 VI
3.4 MULTI-RESOLUTION SPECULAR-PIXEL REPLACEMENT 33
3.4.1 Down-Sampling Process 33
3.4.2 Up-Sampling Process 34
3.5 EXPERIMENTAL RESULTS 35
CHAPTER 4 CONCLUSIONS 40
REFERENCES 42
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