跳到主要內容

臺灣博碩士論文加值系統

(3.238.252.196) 您好!臺灣時間:2022/08/13 23:23
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:李維光
研究生(外文):Li Wei-kuang
論文名稱:以運動分析為基礎之場景變換偵測方法
論文名稱(外文):Motion-aided Algorithms for Detecting Video Shot Transitions
指導教授:賴尚宏
指導教授(外文):Shang-Hong Lai
學位類別:碩士
校院名稱:國立清華大學
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:2002
畢業學年度:90
語文別:英文
論文頁數:41
中文關鍵詞:場景變換運動分析運動估測場景換場
外文關鍵詞:shot change detectionvideomotion estimationabrupt changegradual changedissolvegradual transitionvideo retrieval
相關次數:
  • 被引用被引用:0
  • 點閱點閱:347
  • 評分評分:
  • 下載下載:28
  • 收藏至我的研究室書目清單書目收藏:0
在影視資料檢索系統中,影片分段索引之建立是必要的前置工作。欲快速有效地分割影片,須有一套精準的自動化場景變換偵測方法。場景變換通常分為劇變式和漸變式兩種。傳統的偵測方法常受到影片中物體或攝影機運動的干擾,我們認為降低運動造成的影響,便能有效提昇場景變換偵測的準確率。在這篇論文中,我們利用可容許少量光源變化的光流場計算來估測影片中所有物體的運動,並發展一套漸進式逼近法以求出精確的光流場向量。獲得運動資訊以後,劇變式的場景變換可利用影片中兩張連續影像在運動補償後各個對應區塊的的相似度來判斷。對於漸變式場景變換的偵測,則是利用影像中穩定區域的亮度改變之連續性,經過雙門檻測試(twin-comparison)後決定起始點與終止點。我們的實驗以美國國家標準局(NIST)於2001年舉辦的資料檢索競賽(TREC)中所使用的影片來測試這個系統,並與競賽中其他優秀的系統做比較,證明此篇論文中所提之方法在場景變換偵測準確度上的優異表現。

Video segmentation is fundamental to a number of applications related to video retrieval and analysis. Shot change detection is the initial step of video segmentation and indexing. There are two basic types of shot changes. One is the abrupt change or cut, and the other is the gradual shot transition. The smooth variations of the video feature values in a gradual transition produced by the editing effects are often confused with those caused by camera or object motions. To overcome this difficulty, it is reasonable to estimate the motions and suppress the disturbance caused by them. In this thesis, we explore the possibility to exploit motion and illumination estimation in a video sequence to detect both abrupt and gradual shot changes. A generalized optical flow constraint that includes an illumination parameter to model local illumination changes is employed in the motion and illumination estimation. An iterative process is used to refine the generalized optical flow constraints step by step. A robust measure that is the likelihood ratio of the corresponding motion-compensated blocks in the consecutive frames is used for detecting abrupt changes. For the detection of gradual shot transitions, we compute the average monotony of intensity variations on the stable pixels in the images in a twin-comparison framework. We test the proposed algorithm on a number of video sequences in TREC 2001 and compare the detection results with the best results reported in the TREC 2001 benchmark. The comparisons indicate that the proposed shot change detection algorithm is competitive against the best existing algorithms.

Abstract
Chap 1. Introduction
Chap 2. Related Works
2.1 Uncompressed-Domain Detection Methods
2.2 Compressed-Domain Detection Methods
2.3 Other Detection Methods
Chap 3. Estimation of Optical Flow and Illumination Changes
3.1 The Generalized Optical Flow Constraint
3.2 Iterative Refinement of Optical Flow Computation
Chap 4. Video Shot Change Detection
4.1 Abrupt Shot Change Detection
4.2 Gradual Shot Transition Detection
Chap 5. Experimental Results
5.1 Performance Evaluation
5.2 Benchmarks
5.3 Experimental Results and Comparisons
Chap 6. Conclusion and Future Works
6.1 Conclusion
6.2 Future Research Directions
References

[1] Q. Tian and H.J. Zhang, “Video shot detection and analysis: Content-based approaches”, in Visual Information Representation, Communication, and Image Processing, ed. By C. W. Chen and Y. Q. Zhang, pp. 227-253, Marcel Dekker, New York, 1999.
[2] I. Koprinska and S. Carrato, “Temporal video segmentation: A survey”, Signal Processing: Image communication, Vol. 16, pp. 477-500, 2001.
[3] Ullas Gargi, Rangachar Kasturi, and Susan H. Strayer, Performance characterization of video-shot-change detection methods, IEEE transactions on circuits and systems for video technology, vol. 10, no. 1, February 2000.
[4] G. Ananger and T. D. C. Little, “A survey of technologies for parsing and indexing digital video”, J. Visual Communication and Image Representation, Vol. 7, No. 1, pp. 28-43, 1996.
[5] C.-C. Shih, H.-R. Tyan and H. Y. M. Liao, “Shot change detection based on the Reynolds Transport Theorem”, personal communication.
[6] B. Shahraray, Scene change detection and content-based sampling of video sequences, Proceedings of International Conference on Image Processing, Lausanne 1996.
[7] R. Kasturi, R. Jain, Dynamic vision, in: R. Kasturi, R. Jain(Eds.), Computer Vision: Principles, IEEE Computer Society Press, Washington DC, 1991, pp. 469~480.
[8] U. Gargi, S. Oswald, D. Kosiba, S. Devadiga, R. Kasturi, Evaluation of video sequence indexing and hierarchical video indexing, Proceedings of SPIE Conference on Storage and Retrieval in Image and Video Databases, 1995, pp.1522-1530.
[9] A. Nagasaka, Y. Tanaka, Automatic video indexing and full-video search for object appearances, in: E. Knuth, V.M. Wegner (Eds.), Visual Database Systems II, Elsevier, Amsterdam, 1995, pp.113~127
[10] J. Hafner, H.S. Sawhney, W. Equitz, M. Flickner, W. Niblack, Efficient color histogram indexing for quadratic form distance functions, IEEE trans. pattern analysis machine intelligence., vol. 17, 1995.
[11] H.J. Zhang, A. Kankanhalli, S.W. Smoliar, Automatic partitioning of full-motion video, multimedia systems 1(1), 1993, pp.142~149.
[12] L.F. Cheong, H. Guo, Shot change detection using scene-based constraint, Multimedia tools and applications 14, 2001, pp.175~186.
[13] S.H. Laiand W.K. Li, New video shot change detection algorithm based on accurate motion, and illumination estimation, Proc. SPIE: storage and retrieval for media databases, vol. 4676, 2002.
[14] P. Bouthemy, M. Gelgon, F. Ganansia, A unified approach to shot change detection and camera motion characterization, IEEE transactions on circuits and systems for video technology 9(7), 1999, pp.1030~1044.
[15] B. Yeo, B. Liu, Rapid scene analysis on compressed video, IEEE Trans. Circuits systems video technol. 5(6), 1995, pp.533~544.
[16] F. Arman, A. Hsu, and M.Y. Chiu, Feature management for large video databases, Proc. IS&T/SPIE conf. storage and retrieval for image and video databases I, vol. SPIE 1908, 1993, pp. 2~12.
[17] J. Meng, Y. Juan, and S. F. Chang, Scene change detection in a MPEG compressed video sequence, Proc. SPIE/IS&T Symp. electronic imaging science and technology: digital video compression: algorithms and technologies, vol. 2419, 1995.
[18] J.H. Kuo, J.L. Wu, An efficient algorithm for scene change detection and camera motion characterization using the approach of heterogeneous video transcoding on MPEG compressed videos, Proc. SPIE: storage and retrieval for media databases, vol. 4676, 2002, pp.168~176.
[19] R. Zabih, J. Miller and K. Mai, “A feature-based algorithm for detecting and classifying scene breaks”, Proc. ACM Multimedia’95, pp. 189-200, San Francisco, CA, 1993.
[20] H. Yu, G. Bozdagi, and S. Harrington, “Feature-based hierarchical video segmentation”, Proc. International Conference on Image Processing, Santa Barbara, pp. 498-501, 1997.
[21] S-C Pei and Y-Z Chou, “Efficient MPEG compressed video analysis using macroblock type information”, IEEE Trans. Multimedia, Vol. 1, No. 4, pp. 321-333, 1999.
[22] S.-W. Lee, Y.-M. Kim, and S. W. Choi, “Fast scene change detection using direct feature extraction from MPEG compressed videos”, IEEE Trans. Multimedia, Vol. 2, No. 4, pp. 240-254, Dec. 2000.
[23] C.-L. Huang and B.-Y. Liao, “A robust scene change detection method for video segmentation”, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 11, No. 12, pp. 1281-1288, Dec. 2001.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top