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研究生:林詩詠
研究生(外文):LIM SOO YONG
論文名稱:整合高階模糊派翠網路和特徵點比對之分鏡自動偵測
論文名稱(外文):Automatic Video Shot Boundary Detection Using a Hybrid Approach of HLFPN and Keypoint Matching
指導教授:沈榮麟沈榮麟引用關係
指導教授(外文):Victor R. L. Shen
口試委員:沈榮麟洪偉文林義楠楊政穎黃國軒
口試委員(外文):Victor R. L. Shen
口試日期:2015-07-23
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:資訊工程學系
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:2015
畢業學年度:103
語文別:英文
論文頁數:58
中文關鍵詞:分鏡偵測高階模糊派翠網路特徵點比對加速稳健特徵差值直方圖
外文關鍵詞:Shot Boundary DetectionHigh-Level Fuzzy Petri NetKeypoint MatchingSURFHistogram Difference
相關次數:
  • 被引用被引用:0
  • 點閱點閱:163
  • 評分評分:
  • 下載下載:11
  • 收藏至我的研究室書目清單書目收藏:1
視頻的內容可以帶來許多有用的資訊,分析視頻內容可用於索引、瀏覽和檢索視頻。在這當中分鏡偵測(SBD)是一個最基礎卻重要的步驟。在我們前一篇論文中已經提出了一個能有效針對新聞視頻做出分鏡偵測的方法,本文提出了整合前論文的HLFPN和新的特徵點比對技術來提高分鏡偵測的精準度。首先,利用HLFPN所得出的結果來做判斷,接著再用SURF演算法來移除錯誤和找出漸變鏡頭,此方法可以有效的提高分鏡偵測的精準度並將其應用在不同類型的視頻上。
Shot boundary detection (SBD) is an important and fundamental step in video content analysis such as content-based video indexing, browsing, and retrieval. In this paper, we present a hybrid SBD method by integrating a technique of high-level fuzzy Petri net (HLFPN) and keypoint matching. The HLFPN with histogram difference is executed as a pre-detection. Next, the speeded up robust features (SURF) algorithm that is reliably robust to image affine transformation and illumination variation is used to figure out the possible false shots and gradual transition based on the assumption from HLFPN. The top-down design can effectively lower down the computational complexity of SURF algorithm. The proposed algorithm has increased the precision of SBD and can be applied to different types of videos.
Acknowledgements I
中文摘要 II
Abstract III
Table of Contents IV
List of Figures VI
List of Tables VII
Chapter 1 - Introduction 1
1-1 Motivation and Purposes 1
1-2 Thesis Organization 3
Chapter 2 - Literature Review 4
2-1 Shot Boundary Detection 4
2-2 High-Level Fuzzy Petri Net 5
2-2-1 Definitions 6
2-2-2 Fuzzy Reasoning 9
2-2-3 Fuzzy Reasoning Algorithm 11
2-3 Keypoint Matching 13
2-3-1 SIFT 14
2-3-2 SURF 18
Chapter 3 - The Proposed Approach 21
3-1 Shot Boundary Detection and Feature Extraction 22
3-1-1 Feature Function 1: Gray-Scale Histogram Difference 22
3-1-2 Feature Function 2: Zero Mean Difference for Three Consecutive Frames 23
3-1-3 Feature Function 3: Zero Mean Difference for Five Consecutive Frames 23
3-2 Membership Degrees 23
3-3 Fuzzy Reasoning and Building HLFPN 26
3-4 Keypoint Matching 28
Chapter 4 - Experimental Results 33
4-1 Experimental Environment 33
4-2 Main Results 35
Chapter 5 - Conclusion 39
References 40
Appendix - A 43
List of Datasets 43
Appendix - B 48
Code Fragments 48
Appendix - C 56
Publication 56
Best Paper Award 58

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