指導教授：丁建均臺灣大學：電信工程學研究所林家蓉Lin, Chia-JungChia-JungLin2014-11-302018-07-052014-11-302018-07-052014http://ntur.lib.ntu.edu.tw//handle/246246/264255影像切割一直是計算機視覺中一個重要的研究課題。這是因為電腦很難完全自動地從背景分割出目標，這也導致了互動式影像分割研究的興起 互動式影像分割藉由使用者的引導及修正來切割出理想的結果。然而，雖然稱為“互動”式影像切割，近期的許多研究仍未做到能即時回饋。 在本篇論文中，我們著重於縮短切割系統的反應時間。為了產生即時的結果，我們提出充分利用使用者仍在與系統互動的時間(例如正在標記前景或背景的大概位置)，我們將演算法分為兩部份，其中只有第二部分會造成使用者可能察覺到的系統延遲。我們也提出採用的紋理特徵以降低切割的錯誤率，尤其當前景和背景具有相似的顏色的時候。實驗結果表明，在人眼的反應系統下，我們的演算法和框架可以達到視為“即時”的反應時間，這樣的框架同時也可以被其他演算法拿來應用。Image segmentation has been a major research topic in computer vision. It is hard to segment the object from the background fully automatically, and solving the problem leads to the researches in interactive image segmentation, which incorporates user intervention to guide the segmentation process and to correct anomalies in the segmentation results. Although called “interactive,” real-time response is not yet reachable by many recent methods. In this thesis, we focus on shorten the response time of the segmentation system. To produce real-time result, we propose making use of the time when user interacts with the system. We separate our algorithm into two parts, where the response time is only contributed by the later part. We adopt texture feature to enhance the results in images where foreground and background have similar colors. Experimental results show that our implementation achieves short response time that could be perceived as “instant.” The framework can also be applied by other different algorithms to shorten the response time.口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Main Contribution 2 1.3 Organization 2 Chapter 2 Interactive Image Segmentation 3 2.1 Image Segmentation 3 2.2 Interactive Segmentation 5 2.3 Prior Works on Interactive Segmentation 6 2.3.1 Graph Cut 6 2.3.2 Random Walker 7 2.3.3 Shortest Path (Geodesic) 7 Chapter 3 Graph Cut Method 9 3.1 Preliminaries 9 3.2 Max-Flow/Min-Cut Theorem 10 3.3 Graph Cut 11 3.4 Graph Cut Segmentation 12 3.4.1 Basic Ideas and Background Information 12 3.4.2 Hard Constraints 16 3.5 Issues in Graph Cut Algorithm 19 3.5.1 The Shrinking Bias 19 3.5.2 Efficiency 21 3.5.3 Vulnerability to Noise 21 3.6 Prior Works 22 3.6.1 Speed-up based graph cut 22 3.6.2 Interactive-based graph cut 23 3.6.3 Shape prior-based graph cut 23 3.7 Lazy Snapping[5] 25 Chapter 4 Overview of Superpixel Methods 29 4.1 Introduction 29 4.2 Gradient-Ascent-Based Algorithms 31 4.3 Graph-Based Algorithms 33 4.4 Remarks 34 4.5 Benchmark and Comparison 35 4.5.1 Benchmark 35 4.5.2 Comparison Results 37 Chapter 5 Proposed Interactive Image Segmentation Method 43 5.1 Observation 43 5.2 User Interface (UI) Design 44 5.3 Framework 45 5.4 Desired Properties of Superpixel Segmentation 46 5.5 Proposed Segmentation Algorithm 48 5.5.1 The Regional Term 48 5.5.2 The Boundary Term 50 5.5.3 Max-Flow/Min-Cut Algorithm 50 5.6 Summary 51 Chapter 6 Experimental Results and Discussion 53 6.1 Grabcut Benchmark 53 6.2 Discussion 65 6.2.1 Processing Time 65 6.2.2 Achievable Segmentation Error 70 6.2.3 Graph Cut Segmentation Issues Revisited 71 6.3 Summary 72 Chapter 7 Conclusion and Future Work 73 7.1 Conclusion 73 7.2 Future Work 74 Appendix A A.1 Simple Linear Iterative Clustering Superpixel (SLIC) 75 REFERENCE 815428881 bytesapplication/pdf論文公開時間：2017/08/08論文使用權限：同意無償授權互動式影像切割超像素像切割即時系統thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/264255/1/ntu-103-R01942054-1.pdf