Skip to main content

Panorama Construction from Multi-view Cameras in Outdoor Scenes

  • Chapter
  • First Online:

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 75))

Abstract

The applications of panoramic images are wide spread in computer vision including navigation systems, object tracking, virtual environment creation, among others. In this chapter, the problems of multi-view shooting and the models of geometrical distortions are investigated under the panorama construction in the outdoor scenes. Our contribution are the development of procedure for selection of “good” frames from video sequences provided by several cameras, more accurate estimation of projective parameters in top, middle, and bottom regions in the overlapping area during frames stitching, and also the lighting improvement of the result panoramic image by a point-based blending in a stitching area. Most proposed algorithms have high computer cost because of mega-pixel sizes of initial frames. The reduction of frames sizes, the use of CUDA technique, or the hardware implementation will improve these results. The experiments show good visibility results with high stitching accuracy, if the initial frames were selected well.

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

Buying options

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
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
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

Learn about institutional subscriptions

References

  1. Briggs AJ, Detweiler C, Li Y, Mullen PC, Scharstein D (2006) Matching scale-space features in 1D panoramas. Comput Vis Image Underst 103(3):184–195

    Article  Google Scholar 

  2. Dang TK, Worring M, Bui TD (2011) A semi-interactive panorama based 3D reconstruction framework for indoor scenes. Comput Vis Image Underst 115(11):1516–1524

    Article  Google Scholar 

  3. Zhang W, Cham WK (2012) Reference-guided exposure fusion in dynamic scenes. J Vis Commun Image Represent 23(3):467–475

    Article  Google Scholar 

  4. Chen H (2008) Focal length and registration correction for building panorama from photographs. Comput Vis Image Underst 112(2):225–230

    Article  Google Scholar 

  5. Ni D, Chui YP, Qu Y, Yang X, Qin J, Wong TT, Ho SSH, Heng PA (2009) Reconstruction of volumetric ultrasound panorama based on improved 3D SIFT. Comput Med Imaging Graph 33(7):559–566

    Article  Google Scholar 

  6. Powell I (1994) Panoramic lens. Appl Opt 33(31):7356–7361

    Article  Google Scholar 

  7. Xiong Y, Turkowski K (1997) Creating image-based VR using a self-calibrating fisheye lens. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 237–243

    Google Scholar 

  8. Luong HQ, Goossens B, Philips W (2011) Joint photometric and geometric image registration in the total least square sense. Pattern Recogn Lett 32(15):2061–2067

    Article  Google Scholar 

  9. Fan BJ, Du YK, Zhu LL, Tang YD (2011) A robust template tracking algorithm with weighted active drift correction. Pattern Recogn Lett 32(9):1317–1327

    Article  Google Scholar 

  10. Zhao G, Lin L, Tang Y (2013) A new optimal seam finding method based on tensor analysis for automatic panorama construction. Pattern Recogn Lett 34(3):308–314

    Article  Google Scholar 

  11. Beier T, Neely S (1992) Feature-based image metamorphosis. In: Procedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH’92, vol 26, no 2. pp 35–42

    Google Scholar 

  12. Haenselmann T, Busse M, Kopf S, King T, Effelsberg W (2009) Multi perspective panoramic imaging. Image Vis Comput 27(4):391–401

    Article  Google Scholar 

  13. Pazzi RW, Boukerche A, Feng J, Huang Y (2010) A novel image mosaicking technique for enlarging the field of view of images transmitted over wireless image sensor networks. J Mob Netw Appl 15(4):589–606

    Article  Google Scholar 

  14. Jain DK, Saxena G, Singh VK (2012) Image mosaicking using corner techniques, Int Conf on Communication Systems and Network Technologies 79–84

    Google Scholar 

  15. Yang J, Wei L, Zhang Z, Tang H (2012) Image mosaic based on phase correlation and Harris operator. J Comput Inf Syst 8(6):2647–2655

    Google Scholar 

  16. Kwon OS, Ha YH (2010) Panoramic video using scale invariant feature transform with embedded color-Invariant values. IEEE Trans Consum Electron 56(2):792–798

    Article  Google Scholar 

  17. Kim D, Hong KS (2008) Practical background estimation for mosaic blending with patch-based Markov random fields. Pattern Recogn 41(7):2145–2155

    Article  MATH  Google Scholar 

  18. Bao P, Xu D (1999) Complex wavelet-based image mosaics using edge-preserving visual perception modeling. Comput Graph 23(3):309–321

    Article  Google Scholar 

  19. Bhosle U, Roy SD, Chaudhuri S (2005) Multispectral panoramic mosaicing. Pattern Recogn Lett 26(4):471–482

    Article  Google Scholar 

  20. Agarwala A, Zheng C, Pal C, Agrawala M, Cohen M, Curless B, Salesin D, Szeliski R (2005) Panoramic video textures. ACM Trans Graph 24(3):821–827

    Article  Google Scholar 

  21. Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73

    Article  Google Scholar 

  22. Li C, Ma L (2009) A new framework for feature descriptor based on SIFT. Pattern Recogn Lett 30(5):544–557

    Article  Google Scholar 

  23. Deng H, Zhang L, Ma J, Kang Z (2011) Interactive panoramic map-like views for 3D mountain navigation. Comput Geosci 37(11):1816–1824

    Article  Google Scholar 

  24. Fiala M, Basu A (2002) Hough transform for feature detection in panoramic images. Pattern Recogn Lett 23(14):1863–1874

    Article  MATH  Google Scholar 

  25. Kim DH, Yoon YI, Choi JS (2003) An efficient method to build panoramic image mosaics. Pattern Recogn Lett 24(14):2421–2429

    Article  MATH  Google Scholar 

  26. Zhu Z, Xu G, Riseman EM, Hanson AR (2006) Fast construction of dynamic and multi-resolution 360° panoramas from video sequences. Image Vis Comput 24(1):13–26

    Article  Google Scholar 

  27. Zhu Z, Riseman EM, Hanson AR (2004) Generalized parallel-perspective stereo mosaics from airborne videos. IEEE Trans Pattern Anal Mach Intell 26(2):226–237

    Article  Google Scholar 

  28. Steedly D, Pal C, Szeliski R (2005) Efficiently registering video into panoramic mosaics. In: IEEE International Conference on Computer Vision (ICCV’2005), vol 2. pp 15–21

    Google Scholar 

  29. Langlotz T, Degendorfer C, Mulloni A, Schall G, Reitmayr G, Schmalstieg D (2011) Robust detection and tracking of annotations for outdoor augmented reality browsing. Comput Graph 35(4):831–840

    Article  Google Scholar 

  30. Seitz SM, Dyer CR (1997) Viewing morphing: uniquely predicting scene appearance from basis images. DARPA Image Understanding Workshop, pp 881–887

    Google Scholar 

  31. Hernández-Mier Y, Blondel WCPM, Daula C, Wolf D, Guillemin F (2010) Fast construction of panoramic images for cystoscopic exploration. Comput Med Imaging Graph 34(7):579–592

    Article  Google Scholar 

  32. Fathima AA, Karthik R, Vaidehi V (2013) Image stitching with combined moment invariants and SIFT features. Procedia Comput Sci 19:420–427

    Article  Google Scholar 

  33. Fischler MA, Bolles RC (1881) Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  34. Yan Q, Xu Y, Yang X, Nguyen T (2014) HEASK: Robust homography estimation based on appearance similarity and keypoint correspondences. Pattern Recognit 47(1):368–387

    Article  Google Scholar 

  35. Torr PHS, Zisserman A (2000) MLESAC: a new robust estimator with application to estimating image geometry. Comput Vis Image Underst 78(1):138–156

    Article  Google Scholar 

  36. Chum O, Matas J, Obdrzalek S (2004) Enhancing RANSAC by generalized model optimization. In: Asian Conference on Computer Vision, ACCV. pp 812–817

    Google Scholar 

  37. Nashat S, Abdullah A, Abdullah MZ (2012) Unimodal thresholding for Laplacian-based Canny-Deriche filter. Pattern Recogn Lett 33(10):1269–1286

    Article  Google Scholar 

  38. Shen F, Zhao Y, Jiang X, Suwa M (2009) Recovering high dynamic range by multi-exposure retinex. J Vis Commun Image Represent 20(8):521–531

    Article  Google Scholar 

  39. Dokládal P, Dokládalová E (2011) Computationally efficient, one-pass algorithm for morphological filters. J Vis Commun Image Represent 22(5):411–420

    Article  Google Scholar 

  40. Fernandes LAF, Oliveira MM (2008) Real-time line detection through an improved Hough transform voting scheme. Pattern Recogn 41(1):299–314

    Article  MATH  Google Scholar 

  41. Maxwell EA (1946) Methods of plane projective geometry based on the use of general homogeneous coordinates. Cambridge University Press, Cambridge

    Google Scholar 

  42. Maxwell EA (1951) General homogeneous coordinates in space of three dimensions. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  43. Roberts LG (1965) Homogeneous matrix representations and manipulations of n-dimensional constructs, Technical Representation Document MS 1405, Lincoln Laboratory, MIT, Cambridge

    Google Scholar 

  44. Chum O, Matas J (2002) Randomized ransack with t(d,d) test. British Machine Vision Conference (BMVC’2002), vol 2. pp 448–457

    Google Scholar 

  45. Capel D (2005) An effective bail-out test for ransack consensus scoring. British Machine Vision Conference (BMVC’2005). pp 629–638

    Google Scholar 

  46. Matas J, Chum O (2005) Randomized RANSAC with sequential probability ratio test. In: 10th IEEE International Conference on Computer Vision, vol 2, pp 1727–1732

    Google Scholar 

  47. Chum O, Matas J (2008) Optimal randomized ransac. IEEE Trans Pattern Anal Image Underst 30(8):1472–1482

    Article  Google Scholar 

  48. Raguram R, Frahm JM, Pollefeys M (2008) A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus. In: 10th European Conference on Computer Vision, vol 2. pp 500–513

    Google Scholar 

  49. Cheng CM, Lai SH (2009) A consensus sampling technique for fast and robust model fitting. Pattern Recogn 42(7):1318–1329

    Article  MATH  Google Scholar 

  50. Kiciak P (2011) Bicubic B-spline blending patches with optimized shape. Comput Aided Des 43(2):133–144

    Article  MathSciNet  Google Scholar 

  51. Kineri Y, Wang M, Lin H, Maekawa T (2012) B-spline surface fitting by iterative geometric interpolation/approximation algorithms. Comput Aided Des 44(7):697–708

    Article  Google Scholar 

  52. Meylan L, Alleysson D, Süsstrunk S (2007) Model of retinal local adaptation for the tone mapping of color filter array images. J Opt Soc Am A: 24(9):2807–2816

    Article  Google Scholar 

  53. Favorskaya M, Pakhirka A (2012) A way for color image enhancement under complex luminance conditions. In: Watanabe T, Watada J, Takahashi N, Howlett RJ, Jain LC (eds) Intelligent interactive multimedia: systems and services. Springer, Berlin

    Google Scholar 

  54. Zhang Y, Pajarola R (2007) Deferred blending: Image composition for single-pass point rendering. Comput and Graph 31(2):175–189

    Article  Google Scholar 

  55. Sun J, Zhu H, Xu Z, Han C (2013) Poisson image fusion based on Markov random field fusion model. Inf Fusion 14(3):241–254

    Article  Google Scholar 

  56. Mills A, Dudek G (2009) Image stitching with dynamic elements. Image Vis Comput 27(10):1593–1602

    Article  Google Scholar 

  57. Gómez F, Romero E (2011) Rotation invariant texture characterization using a curvelet based descriptor. Pattern Recogn Lett 32(16):2178–2186

    Article  Google Scholar 

  58. Yang S, Wang M, Jiao L, Wua R, Wang Z (2010) Image fusion based on a new contourlet packet. Inf Fusion 11(2):78–84

    Article  Google Scholar 

  59. Gracias N, Mahoor M, Negahdaripour S, Gleason A (2009) Fast image blending using watersheds and graph cuts. Image Vis Comput 27(5):597–607

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lakhmi C. Jain .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Jain, L.C., Favorskaya, M.N., Novikov, D. (2015). Panorama Construction from Multi-view Cameras in Outdoor Scenes. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Control Systems-2. Intelligent Systems Reference Library, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-319-11430-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11430-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11429-3

  • Online ISBN: 978-3-319-11430-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics