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Video Preprocessing

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Book cover Video Text Detection

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Extracting texts from video always faces variations in font style, size, color, orientation, and brightness; thus, video preprocessing techniques are required to reduce the complexity of the succeeding steps consisting of video text detection, localization, segmentation, recognition, and script identification. This chapter gives a brief overview of the preprocessing techniques that are often used in video text detection. After introducing image preprocessing operators, we discuss several color-based and texture-based preprocessing techniques, respectively. Since image segmentation plays an important role in video text detection, we then introduce several image segmentation approaches. Next, the motion analysis technique which is helpful to improve the efficiency or the accuracy of video text detection by tracing text from temporal frames is introduced. Most of the introduced preprocessing operators and methods have been realized by MATLAB or OpenCV (Open Source Computer Vision Library), and readers can make use of these open sources for practice.

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References

  1. Jung K, In Kim K, Jain AK (2004) Text information extraction in images and video: a survey. Pattern Recog 37(5):977–997

    Article  Google Scholar 

  2. Lienhart RW, Stuber F (1996) Automatic text recognition in digital videos. Proc SPIE 2666(3):180–188

    Article  Google Scholar 

  3. Crane R (1996) Simplified approach to image processing: classical and modern techniques in C. Prentice Hall PTR. 317

    Google Scholar 

  4. Szeliski R (2010) Computer vision: algorithms and applications. Springer, New York

    Google Scholar 

  5. Kopf J et al (2007) Capturing and viewing gigapixel images. ACM Trans Graph 26(3):93

    Article  Google Scholar 

  6. Roberts LG (1963) Machine perception of three-dimensional solids, DTIC Document

    Google Scholar 

  7. Engel K et al (2006) Real-time volume graphics: AK Peters, Limited

    Google Scholar 

  8. Ritter GX, Wilson JN (1996) Handbook of computer vision algorithms in image algebra, vol 1. Citeseer

    Google Scholar 

  9. Hasan YMY, Karam LJ (2000) Morphological text extraction from images. IEEE Trans Image Process 9(11):1978–1983

    Article  Google Scholar 

  10. Jae-Chang S, Dorai C, Bolle R (1998) Automatic text extraction from video for content-based annotation and retrieval. In: Proceedings of the fourteenth international conference on pattern recognition, 1998

    Google Scholar 

  11. Kim H-K (1996) Efficient automatic text location method and content-based indexing and structuring of video database. J Vis Commun Image Represent 7(4):336–344

    Article  Google Scholar 

  12. Jain AK, Yu BIN (1998) Automatic text location in images and video frames. Pattern Recogn 31(12):2055–2076

    Article  Google Scholar 

  13. Zhong Y, Karu K, Jain AK (1995) Locating text in complex color images. Pattern Recogn 28(10):1523–1535

    Article  Google Scholar 

  14. Wong EK, Chen M (2003) A new robust algorithm for video text extraction. Pattern Recogn 36(6):1397–1406

    Article  MATH  Google Scholar 

  15. Shivakumara P, Trung Quy P, Tan CL (2011) A laplacian approach to multi-oriented text detection in video. IEEE Trans Pattern Anal Mach Intell 33(2):412–419

    Article  Google Scholar 

  16. Kim KI, Jung K, Kim JH (2003) Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Trans Pattern Anal Mach Intell 25(12):1631–1639

    Article  MathSciNet  Google Scholar 

  17. Ye Q et al (2007) Text detection and restoration in natural scene images. J Vis Commun Image Represent 18(6):504–513

    Article  Google Scholar 

  18. Shivakumara P, Trung Quy P, Tan CL (2009) A robust wavelet transform based technique for video text detection. In: ICDAR ‘09. 10th international conference on document analysis and recognition, 2009

    Google Scholar 

  19. Zhong J, Jian W, Yu-Ting S (2009) Text detection in video frames using hybrid features. In: International conference on machine learning and cybernetics, 2009

    Google Scholar 

  20. Zhao M, Li S, Kwok J (2010) Text detection in images using sparse representation with discriminative dictionaries. Image Vis Comput 28(12):1590–1599

    Article  Google Scholar 

  21. Shivakumara P, Trung Quy P, Tan CL (2010) New Fourier-statistical features in RGB space for video text detection. Circ Syst Video Technol IEEE Trans 20(11):1520–1532

    Article  Google Scholar 

  22. Rongrong J et al (2008) Directional correlation analysis of local Haar binary pattern for text detection. In: IEEE international conference on multimedia and expo, 2008

    Google Scholar 

  23. Hanif SM, Prevost L (2007) Text detection in natural scene images using spatial histograms. In: 2nd workshop on camera based document analysis and recognition, Curitiba

    Google Scholar 

  24. Chucai Y, YingLi T (2011) Text detection in natural scene images by Stroke Gabor Words. In: International conference on document analysis and recognition (ICDAR), 2011

    Google Scholar 

  25. Qian X et al (2007) Text detection, localization, and tracking in compressed video. Signal Process Image Commun 22(9):752–768

    Article  Google Scholar 

  26. ZHANG YJ (2002) Image engineering and related publications. Int J Image Graph 02(03):441–452

    Article  Google Scholar 

  27. Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comp Vis Graph Image Process 29(1):100–132

    Article  Google Scholar 

  28. Shapiro LG, Stockman GC (2001) Computer vision. Prentice-Hall, New Jersey, pp 279–325

    Google Scholar 

  29. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27

    Google Scholar 

  30. Saraf Y (2006) Algorithms for image segmentation, Birla Institute of Technology and Science

    Google Scholar 

  31. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. Pattern Anal Mach Intell IEEE Trans on 24(5):603–619

    Article  Google Scholar 

  32. Pantofaru C, Hebert M (2005) A comparison of image segmentation algorithms. Robotics Institute, p 336

    Google Scholar 

  33. Felzenszwalb P, Huttenlocher D (2004) Efficient graph-based image segmentation. Int J Comp Vis 59(2):167–181

    Article  Google Scholar 

  34. Shi J, Malik J (2000) Normalized cuts and image segmentation. Pattern Anal Mach Intell IEEE Trans 22(8):888–905

    Article  Google Scholar 

  35. Palma D, Ascenso J, Pereira F (2004) Automatic text extraction in digital video based on motion analysis. In: Campilho A, Kamel M (eds) Image analysis and recognition. Springer, Berlin, pp 588–596

    Chapter  Google Scholar 

  36. Brox T et al (2004) High accuracy optical flow estimation based on a theory for warping. In: Pajdla T, Matas J (eds) Computer vision – ECCV 2004. Springer, Berlin, pp 25–36

    Chapter  Google Scholar 

  37. Beauchemin SS, Barron JL (1995) The computation of optical flow. ACM Comput Surv 27(3):433–466

    Article  Google Scholar 

  38. Anandan P (1989) A computational framework and an algorithm for the measurement of visual motion. Int J Comp Vis 2(3):283–310

    Article  Google Scholar 

  39. Weickert J, Schnörr C (2001) A theoretical framework for convex regularizers in PDE-based computation of image motion. Int J Comp Vis 45(3):245–264

    Article  MATH  Google Scholar 

  40. Mémin E, Pérez P (2002) Hierarchical estimation and segmentation of dense motion fields. Int J Comp Vis 46(2):129–155

    Article  MATH  Google Scholar 

  41. Sun D et al (2008) Learning optical flow. In: Forsyth D, Torr P, Zisserman A (eds) Computer vision – ECCV 2008. Springer, Berlin, pp 83–97

    Chapter  Google Scholar 

  42. Black MJ, Anandan P (1996) The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comp Vis Image Underst 63(1):75–104

    Article  Google Scholar 

  43. Głowacz A, Mikrut Z, Pawlik P (2012) Video detection algorithm using an optical flow calculation method. In: Dziech A, Czyżewski A (eds) Multimedia communications, services and security. Springer, Berlin, pp 118–129

    Chapter  Google Scholar 

  44. Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203

    Article  Google Scholar 

  45. Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: IJCAI

    Google Scholar 

  46. Kui L et al (2010) Optical flow and principal component analysis-based motion detection in outdoor videos. EURASIP J Adv Signal Proc

    Google Scholar 

  47. Zhao Y et al (2011) Real-time video caption detection. In: Proceedings of the ninth IAPR international workshop on graphics recognition

    Google Scholar 

  48. Gonzalez RC, Woods RE, Eddins SL Digital image processing using matlab. Prentice Hall

    Google Scholar 

  49. Nixon MS, Aguado AS Feature extraction & image processing for computer vision, 3rd edn. Academic

    Google Scholar 

  50. Forsyth DA, Ponce J Computer vision: a modern approach, 2nd edn. Prentice Hall Press

    Google Scholar 

  51. Petrou M, Petrou C Image processing: the fundamentals. Wiley

    Google Scholar 

  52. Haralick RM, Shanmugam K (1973) Its’Hak Dinstein. Textual features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Tong Lu

  2. Faculty of CSIT, University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  3. National University of Singapore, Singapore, Singapore

    Chew Lim Tan

  4. Multimedia Software Engineering Research Center, City University of Hong Kong, Kowloon Tong, Hong Kong SAR

    Wenyin Liu

Authors
  1. Tong Lu
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  2. Shivakumara Palaiahnakote
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  3. Chew Lim Tan
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  4. Wenyin Liu
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© 2014 Springer-Verlag London

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Lu, T., Palaiahnakote, S., Tan, C.L., Liu, W. (2014). Video Preprocessing. In: Video Text Detection. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6515-6_2

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  • DOI: https://doi.org/10.1007/978-1-4471-6515-6_2

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6514-9

  • Online ISBN: 978-1-4471-6515-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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