Skip to main content

Digital Video Stabilization in Static and Dynamic Scenes

  • Chapter
  • First Online:
Computer Vision in Control Systems-1

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 73))

Abstract

The digital video stabilization is oriented on the removal of unintentional motions from video sequences caused by camera vibrations under external conditions, motion of robots stabilized platforms in a rugged landscape, a sea, oceans, or jitters during a non-professional hand-held shooting. The approaches for digital video stabilization in static and dynamic scenes are similar. However, objectively the analysis of dynamic scenes is needed in advanced intelligent methods. Several sequential stages include the choice of the key frames, the local and global motion estimations, the jitters compensation algorithm, the inpainting of frames boundaries, and the blurred frames restoration, for which the novel methods and algorithms were developed. The proposed application of fuzzy logic operators improves the separation results between the unwanted motion and the real motion of rigid objects. The corrective algorithm compensates the unwanted motion in frames; thereby the scene is aligned. The quality of stabilization in test video sequences was estimated by Peak Signal to Noise Ratio (PSNR) and Interframe Transformation Fidelity (ITF) metrics. During experiments, the PSNR and ITF estimations were received for six video sequences received from the static camera and eight video sequences received from the moving camera. The ITF estimations increase up on 3–4 dB or 15–20 % relative to the original video sequences.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Battiato S, Lukac R (2008) Video stabilization techniques. In: Furht B (ed) Encyclopedia of multimedia. Springer Science+Business Media, New York

    Google Scholar 

  2. Chen H, Liang CK, Peng YC, Chang HA (2007) Integration of digital stabilizer with video codec for digital video cameras. IEEE Trans Circuits Syst Video Technol 17(7):801–813

    Article  Google Scholar 

  3. Rawat P, Singhai J (2011) Review of motion estimation and video stabilization techniques for hand held mobile video. Int J of Signal and Image Process 2(2):159–168

    Article  Google Scholar 

  4. Leghmizi S, Liu S (2011) A survey of fuzzy control for stabilized platforms. Int J of Comput Sci Eng Surv (IJCSES) 2(3):48–57

    Article  Google Scholar 

  5. Battiato S, Bruna AR, Puglisi G (2010) A robust block based image/video registration approach for mobile imaging devices. IEEE Trans Multimedia 12(7):622–635

    Article  Google Scholar 

  6. Bosco A, Bruna A, Battiato S, Bella G, Puglisi G (2008) Digital video stabilization through curve warping techniques. J IEEE Trans Consum Electron 54(2):220–224

    Article  Google Scholar 

  7. Lee J, Park Y, Lee S, Paik J (2009) Statistical region selection for robust image stabilization using feature-histogram. In: IEEE international conference on image processing (ICIP), pp 1553–1556

    Google Scholar 

  8. Liu F, Gleicher M, Jin H, Agarwala A (2009) Content-preserving warps for 3D video stabilization. ACM Trans Graph (SIGGRAPH 2009) 28(3):44:1–44:9

    Google Scholar 

  9. Wang JM, Chou HP, Chen SW, Fuh CS (2009) Video stabilization for a hand-held camera based on 3D motion model. In: IEEE international conference on image processing (ICIP), pp 3477–3481

    Google Scholar 

  10. Nestares O, Gat Y, Haussecker H, Kozinsev I (2010) Video stabilization to a global 3D frame of reference by fusing orientation sensor and image alignment data. In: 9th IEEE international symposium on mixed augmented reality (ISMAR) 257–258

    Google Scholar 

  11. Hansen M, Anandan P, Dana K, van der Wal G, Burt PJ (1994) Real time scene stabilization and mosaic construction. Image understanding Workshop, Defense Advanced Research Project Agency (DARPA), pp 457–465

    Google Scholar 

  12. Favorskaya M (2012) Motion estimation for object analysis and detection in videos. In: Kountchev R, Nakamatsu K (eds) Advances in reasoning-based image processing, analysis and intelligent systems. Springer, Berlin Heidelberg

    Google Scholar 

  13. Babagholami-Mohamadabadi B, Bagheri-Khaligh A, Hassanpour R (2012) Digital video stabilization using radon transform. Digital Image Comput: Tech Appl (DICTA) 12:1–8

    Google Scholar 

  14. Favorskaya M, Levtin K (2014) Early smoke detection in outdoor space by spatio-temporal clustering using a single video camera. In: Tweedale JW, Jain LC (eds) Recent advances in knowledge-based paradigms and applications. Springer, Berlin Heidelberg

    Google Scholar 

  15. Shakoor MH, Moattari M (2011) Statistical digital image stabilization. J Eng Technol Res 3(5):161–167

    Google Scholar 

  16. Zhou M, Asari VK (2011) A fast video stabilization system based on speeded-up robust features advances. In: Zhou M, Asari VK (eds) Adv Vis Comput., LNCSSpringer, Berlin Heidelberg

    Google Scholar 

  17. Ertürk S (2002) Real-time digital image stabilization using Kalman filters. Real Time Image 8(4):317–328

    Article  MATH  Google Scholar 

  18. Pinto B, Anurenjan PR (2011) Video stabilization using speeded up robust features. In: International conference on communications and signal processing (ICCSP), pp 527–531

    Google Scholar 

  19. Chang HC, Lai SH, Lu KR (2006) A robust real-time video stabilization algorithm. J Vis Commun Image Represent 17(3):659–673

    Article  Google Scholar 

  20. Alzoubi H, Pan WD (2008) Fast and accurate global motion estimation algorithm using pixel subsampling. Inf Sci 178:3415–3425

    Article  Google Scholar 

  21. Del Blanco CR, Jaureguizar F, Salgado L, Garcıa N (2008) Automatic feature-based stabilization of video with intentional motion through a particle filter. In: del-Blanco CR, Jaureguizar F, Salgado L, García N (eds) Advanced concepts for intelligent vision systems (ACIVS) LNCS. Springer, Berlin Heidelberg

    Chapter  Google Scholar 

  22. Yang J, Schonfeld D, Mohamed M (2009) Robust video stabilization based on particle filter tracking of projected camera motion. IEEE Trans Circ Syst Video Technol 19(7):945–954

    Article  Google Scholar 

  23. Litvin A, Konrad J, Karl WC (2003) Probabilistic video stabilization using Kalman filtering and mosaicking. Image and video communication and processing 2003. SPIE-IS&T Electron Imaging SPIE 5022:663–674

    Google Scholar 

  24. Güllü MK, Ertürk S (2004) Membership function adaptive fuzzy filter for image sequence stabilization. IEEE Trans Consum Electron 50(1):1–7

    Article  Google Scholar 

  25. Battiato S, Puglisi G, Bruna AR (2008) A robust video stabilization system by adaptive motion vectors filtering. In: IEEE international conference on multimedia and expo, pp 373–376

    Google Scholar 

  26. Tanakian MJ, Rezaei M, Mohanna F (2012) Digital video stabilizer by adaptive fuzzy filtering. EURASIP J Image Video Process 2012. SpringerOpen. doi:10.1186/1687-5281-2012-21

  27. Puglisi G, Battiato S (2011) A robust image alignment algorithm for video stabilization purposes. IEEE Trans Circ Syst Video Technol 21(10):1390–1400

    Article  Google Scholar 

  28. Kyriakoulis N, Gasteratos A (2008) A recursive fuzzy system for efficient digital image stabilization. J Adv Fuzzy Syst 8:1–8

    Article  Google Scholar 

  29. Amanatiadis AA, Andreadis I (2010) Digital image stabilization by independent component analysis. IEEE Trans Instrum Measur 59(7):1755–1763

    Article  Google Scholar 

  30. Shen Y, Guturu P, Damarla T, Buckles BP, Namuduri KR (2009) Video stabilization using principal component analysis and scale invariant feature transform in particle filter framework. IEEE Trans Consum Electron 55(3):1714–1721

    Article  Google Scholar 

  31. Tsai D, Lai S (2008) Defect detection in periodically patterned surfaces using independent component analysis. Pattern Recogn 41(9):2812–2832

    Article  MATH  Google Scholar 

  32. Kim N, Lee H, Lee J (2008) Probabilistic global motion estimation based on Laplacian two-bit plane matching for fast digital image stabilization, EURASIP J Adv Signal Process 1–10

    Google Scholar 

  33. Pun CM, Lee MC (2003) Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans Pattern Anal Mach Intell 25(5):590–603

    Article  Google Scholar 

  34. Hu R, Shi R, Shen I, Chen W (2007) Video stabilization using scale invariant features. In: IEEE 11th international conference on information visualization IV’07, pp 871–877

    Google Scholar 

  35. Tanakian MJ, Rezaei M, Mohanna F (2010) Digital video stabilization system by adaptive motion vector validation and filtering. In: International conference on communication engineering, pp 165–183

    Google Scholar 

  36. Tanakian MJ, Rezaei M, Mohanna F (2011) Digital video stabilization system by adaptive fuzzy filtering. In: 19th European signal process conference, pp 318–322

    Google Scholar 

  37. Matsushita Y, Ofek E, Ge W, Tang X, Shum HY (2006) Full-frame video stabilization with motion inpainting. IEEE Trans Pattern Anal Mach Intell 28(7):1150–1163

    Article  Google Scholar 

  38. Pang D, Chen H, Halawa S (2010) Efficient video stabilization with dual-tree complex wavelet transform. EE368 project report

    Google Scholar 

  39. Liu F, Gleicher M, Wang J, Jin H, Agarwala A (2011) Subspace video stabilization. ACM Trans Graph 30(1):4:1–4:10

    Google Scholar 

  40. Zhang G, Hua W, Qin X, Shao Y, Bao H (2009) Video stabilization based on a 3D perspective camera model. Vis Comput 25(11):997–1008

    Article  Google Scholar 

  41. Chen PH, Chen HM, Hung KJ, Fang WH, Shie MC, Lai F (2006) Markov model fuzzy-reasoning based algorithm for fast block motion estimation. J Vis Commun Image Represent 17(1):131–142

    Article  Google Scholar 

  42. Benmoussat N, Belbachir MF, Benamar B (2007) Motion estimation and compensation from noisy image sequences: a new filtering scheme. Image Vis Comput 25(5):686–694

    Article  Google Scholar 

  43. Liu X, Cong W (2010) Hybrid-template adaptive motion estimation algorithm based on block matching. Int Conf Comput Commun Technol Agric Eng. doi:10.1109/CCTAE.2010.5543459

    Google Scholar 

  44. Boudlal A, Nsiri B, Aboutajdine D (2010) Modeling of video sequences by gaussian mixture: application in motion estimation by block matching method. EURASIP J Adv Signal Process. doi:10.1155/2010/210937

    Google Scholar 

  45. Jang SW, Pomplun M, Kim GY, Choi HI (2005) Adaptive robust estimation of affine parameters from block motion vectors. Image Vis Comput 23(14):1250–1263

    Article  Google Scholar 

  46. Basarab A, Liebgott H, Morestin F, Lyshchik A, Higashi T, Asato R, Delachartre P (2008) A method for vector displacement estimation with ultrasound images and its application for thy-roid nodular disease. Med Image Anal 12(3):259–274

    Article  Google Scholar 

  47. Moreno-Garcia J, Rodriguez-Benitez L, Fernandez-Caballero A, Lopez MT (2010) Video sequence motion tracking by fuzzification techniques. Appl Soft Comput 10(1):318–331

    Article  Google Scholar 

  48. Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science Inc., New York

    Google Scholar 

  49. Battiato S, Gallo G, Puglisi G, Scellato S (2009) Fuzzy-based motion estimation for video stabilization using SIFT interest points. In: IS&T-SPIE electronic imaging symposium, digital photography V EI-7250, pp 1–8

    Google Scholar 

  50. Pan JY, Hu B, Zhang JQ (2008) Robust and accurate object tracking under various types of occlusions. IEEE Trans CSVT 18:223–236

    Google Scholar 

  51. O’Hara S, Lui YM, Draper BA (2012) Using a product manifold distance for unsupervised action recognition. Image Vis Comput 30(3):206–216

    Article  Google Scholar 

  52. Belardinelli A, Carbone A, Schneider WX (2013) Classification of multi-scale spatiotemporal energy features for video segmentation and dynamic objects prioritization. Pattern Recogn Lett 34(7):713–722

    Article  Google Scholar 

  53. Panagiotakis C, Doulamis AD, Tziritas G (2009) Equivalent key frames selection based on iso-content principles. IEEE Trans Circuits Syst Video Technol 19(3):447–451

    Article  Google Scholar 

  54. Chao GC, Tsai YP, Jeng SK (2010) Augmented keyframe. J Vis Commun Image Represent 21(7):682–692

    Article  Google Scholar 

  55. Cheung V, Frey BJ, Jojic N (2005) Video epitomes. IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR) 1:42–49

    Google Scholar 

  56. Yu G, Li Z, Suyu W, Lansun S (2009) A novel scene cut detection method in H.264/AVC compression domain. Chin J Electron 18(4):695–699

    Google Scholar 

  57. Jokovic J, Horevic D (2009) Scene cut detection in video by using combination of spatial-temporal video characteristics. In: 9th international conference on telecommunication in modern satellite, cable, and broadcasting services, pp 479–482

    Google Scholar 

  58. Xiang S, Meng G, Wang Y, Pan C, Zhang C (2012) Image deblurring with matrix regression and gradient evolution. Pattern Recogn 45(6):2164–2179

    Article  MATH  Google Scholar 

  59. Cho S, Wang J, Lee S (2011) Handling outliers in non-blind image deconvolution. In: International conference on computer vision ICCV11, pp 495–502

    Google Scholar 

  60. Xu YQ, Wang L, Hu XY, Peng SL (2012) Single-image blind deblurring for non-uniform camera-shake blur. In: ACCV12, vol 7726, pp 336–348

    Google Scholar 

  61. Whyte O, Sivic J, Zisserman A, Ponce J (2010) Non-uniform deblurring for shaken images. In: Computer vision and pattern recognition CVPR 2010, pp 491–498

    Google Scholar 

  62. Cai JF, Ji H, Liu C, Shen Z (2009) Blind motion deblurring using multiple images. J Comput Phys 228(14):5057–5071

    Article  MathSciNet  MATH  Google Scholar 

  63. Favorskaya M, Pyankov D, Popov A (2013) Motion estimations based on invariant moments for frames interpolation in stereovision. Proc Comput Sci 22:1102–1111

    Article  Google Scholar 

  64. Kwatra V, Essa I, Bobick A, Kwatra N (2005) Texture optimization for example-based synthesis. ACM Trans Graph 24(3):795–802

    Article  Google Scholar 

  65. Favorskaya M, Damov M, Zotin A (2013) Accurate spatio-temporal reconstruction of missing data in dynamic scenes. Pattern Lett Recognit 34(14):1694–1700

    Article  Google Scholar 

  66. Lim T, Han B, Han JH (2012) Modeling and segmentation of floating foreground and background in videos. Pattern Recognit 45(4):1696–1706

    Article  MATH  Google Scholar 

  67. Lowe D (2004) Distinctive image features from scale-invariant key points. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  68. Zhong S, Wang J, Yan L, Kang L, Cao Z (2013) A real-time embedded architecture for SIFT. J Syst Archit 59(1):16–29

    Article  Google Scholar 

  69. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  70. Kerr D, Coleman SA, Scotney BW (2011) Finite element laplacian feature detector. In: IAPR, conference on machine vision and applications (MVA 2011), pp 381–384

    Google Scholar 

  71. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: 7th international joint conference on artificial intelligence (IJCAI), vol 2, pp 674–679

    Google Scholar 

  72. Liao B, Du M, Hu J (2010) Color optical flow estimation based on gradient fields with extended constraints. In: International conference on networking and information technology, pp 279–283

    Google Scholar 

  73. Lee KJ, Yun ID, Lee SU (2013) Adaptive large window correlation for optical flow estimation with discrete optimization. Image Vis Comput 31(9):631–639

    Article  Google Scholar 

  74. Nayak NM, Zhu Y, Roy-Chowdhury AK (2013) Vector field analysis for multi-object behavior modeling. Image Vis Comput 31(6–7):460–472

    Article  Google Scholar 

  75. Kim KS, Jang DS, Choi HI (2007) Real time face tracking with pyramidal Lucas-Kanade feature tracker. In: International conference on computational science and its applications ICCSA’07 Part I, pp 1074–1082

    Google Scholar 

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

    Article  Google Scholar 

  77. Briassouli A, Kompatsiaris I (2009) Robust temporal activity templates using higher order statistics. IEEE Trans Image Process 18(12):2756–2768

    Article  MathSciNet  Google Scholar 

  78. Kim HH, Kim YH (2010) Toward a conceptual framework of key-frame extraction and storyboard display for video summarization. J Am Soc Inf Sci Technol 61(5):927–939

    Article  Google Scholar 

  79. Ejaz N, Baik SW (2011) Weighting low level frame difference features for key frame extraction using fuzzy comprehensive evaluation and indirect feedback relevance mechanism. Int J Phys Sci 6(14):3377–3388

    Google Scholar 

  80. Ejaz N, Tariq TB, Baik SW (2012) Adaptive key frame extraction for video summarization using an aggregation mechanism. J Vis Commun Image Represent 23(7):1031–1040

    Article  Google Scholar 

  81. Brown RG, Hwang PYC (1992) Introduction to random signals and applied Kalman Filtering. Wiley, New York

    MATH  Google Scholar 

  82. Han ZJ, Ye QX, Jiao JB (2008) Online feature evaluation for object tracking using Kalman Filter. In: 19th International conference on pattern recognition ICPR 2008, pp 1–4

    Google Scholar 

  83. Wang J, Chen X, Gao W (2005) Online selecting discriminative tracking features using particle filter. In: IEEE international conference on computer vision and pattern recognition CVPR’2005, vol 2, pp 1037–1042

    Google Scholar 

  84. Han Z, Ye Q, Jiao J (2011) Combined feature evaluation for adaptive visual object tracking. Comput Vis Image Underst 115(1):69–80

    Article  Google Scholar 

  85. Saffari A, Leistner C, Santner J, Godec M, Bischof H (2009) On-line random forests. In: IEEE 12th international conference on computer vision workshops (ICCV Workshops), pp 1393–1400

    Google Scholar 

  86. Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. In: 10th European conference on computer vision ECCV ‘08 Part I, pp 234–247

    Google Scholar 

  87. Santner J, Leistner C, Saffari A, Pock T, Bischof H (2010) PROST parallel robust online simple tracking. In: IEEE conference on computer vision and pattern recognition, pp 723–730

    Google Scholar 

  88. Kalal Z, Matas J, Mikolajczyk K (2010) P–N learning: bootstrapping binary classifiers by structural constraints. IEEE conference on computer vision and pattern recognition, pp 49–56

    Google Scholar 

  89. Felzenszwalb P, Girshick R, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  90. Gall J, Lempitsky V (2009) Class-specific Hough forests for object detection. In: IEEE conference on computer vision and pattern recognition CVPR 2009, pp 1022–1029

    Google Scholar 

  91. Maji S, Malik J (2009) Object detection using a max-margin Hough transform. In: IEEE conference on computer vision and pattern recognition, CVPR 2009, pp 1038–1045

    Google Scholar 

  92. Godec M, Roth PM, Bischof H (2011) Hough-based tracking of non-rigid objects. In: IEEE international conference on computer vision and image understanding computer vision (ICCV 2011), pp 81–88

    Google Scholar 

  93. Rother C, Kolmogorov V, Blake A (2004) GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314

    Article  Google Scholar 

  94. Ozuysal M, Calonder M, Lepetit V, Fua P (2010) Fast keypoint recognition using random ferns. IEEE Trans Pattern Anal Mach Intell 32(3):448–461

    Article  Google Scholar 

  95. Villamizar M, Moreno-Noguer F, Andrade-Cetto J, Sanfeliu A (2010) Efficient rotation invariant object detection using boosted Random Ferns. In: IEEE conference on computer vision and pattern recognition, CVPR 2010, pp 1038–1045

    Google Scholar 

  96. Godec M, Leistner C, Saffari A, Bischof H (2010) On-line random naive bayes for tracking. In: International conference on pattern recognition ICPR 2010, pp 3545–3548

    Google Scholar 

  97. Rodriguez-Sánchez R, García JA, Fdez-Valdivia J (2013) Image inpainting with nonsubsampled contourlet transform. Pattern Recogn Lett 34(13):1508–1518

    Article  Google Scholar 

  98. Kim S, Shina J, Paik J (2003) Real-time iterative framework of regularized image restoration and its application to video enhancement. Real-Time Imaging 9(1):61–72

    Article  Google Scholar 

  99. Szeliski R (2006) Image Alignment and Stitching: a tutorial. Found Trends Comput Graph Vis 2(1):1–104

    Article  Google Scholar 

  100. Eden A, Uyttendaele M, Szeliski R (2006) Seamless image stitching of scenes with large motions and exposure differences. In: IEEE computer society conference on computer vision and pattern recognition, vol 2, pp 2498–2505

    Google Scholar 

  101. Ma LM, Wu ZM (2009) Approximation to the k-th derivatives by multiquadric quasi-interpolation method. J Comput Appl Math 231(2):925–932

    Article  MathSciNet  MATH  Google Scholar 

  102. Colonnese S, Randi R, Rinauro S, Scarano G (2010) Fast image interpolation using circular harmonic functions. European workshop on visual information processing EUVIP 2010, pp 114–118

    Google Scholar 

  103. Lee Y, Yoon J (2010) Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans Image Process 19(10):2682–2692

    Article  MathSciNet  Google Scholar 

  104. Gao Q, Wu Z, Zhang S (2011) Applying multiquadric quasi-interpolation for boundary detection. Comput Math Appl 62(12):4356–4361

    Article  MathSciNet  MATH  Google Scholar 

  105. Zhang X, Wu X (2008) Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation. IEEE Trans Image Process 17(6):887–896

    Article  MathSciNet  Google Scholar 

  106. Takeda H, van Beek P, Milanfar P (2008) Spatio-temporal video interpolation and denoising using motion-assisted steering kernel MASK regression. In: International conference on image processing ICIP 2008, pp 637–640

    Google Scholar 

  107. Ceccarelli M (2007) A finite Markov random field approach to fast edge-preserving image recovery. Image Vis Comput 25(6):792–804

    Article  Google Scholar 

  108. Li M, Nguyen TQ (2008) Markov random field model based edge-directed image interpolation. IEEE Trans Image Process 17(7):1121–1128

    Article  MathSciNet  Google Scholar 

  109. Nemirovsky S, Porat M (2009) On texture and image interpolation using Markov models. Sig Process Image Commun 24(3):139–157

    Article  Google Scholar 

  110. Simonyan K, Vatolin D (2009) Edge-directed interpolation in a bayesian frame-work. In: British machine vision conference, vol 10, pp 1521–1527

    Google Scholar 

  111. Colonnese S, Rinauro S, Scarano G (2011) Markov random fields using complex line process: an application to Bayesian image restoration. European workshop on visual information processing EUVIP 2011, pp 30–35

    Google Scholar 

  112. Colonnese S, Rinauro S, Scarano G (2013) Bayesian image interpolation using Markov random fields driven by visually relevant image features. Sig Process Image Commun 28(8):967–983

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Margarita N. Favorskaya .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Favorskaya, M.N., Jain, L.C., Buryachenko, V. (2015). Digital Video Stabilization in Static and Dynamic Scenes. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Control Systems-1. Intelligent Systems Reference Library, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-10653-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10653-3_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10652-6

  • Online ISBN: 978-3-319-10653-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics