Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

A cue integration method for anaglyph image partition

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Image content analysis is important for automated image organization, labeling, and search. Partitioning an image into meaningful regions is one of the fundamental problems in image analysis. Anaglyph images and videos are more and more popular, such as in Flickr and YouTube. The anaglyph images provide disparity cue in a single image, which could be useful for image analysis. This paper exploits disparity cue for image partition. An image partition method for anaglyph is proposed. The disparity or depth cue is integrated with the traditional single-view image segmentation. A concept called dominant disparity is proposed, corresponding to each single-view image segment, which largely tolerates the disparity errors and image over-segmentations. A cue integration algorithm is developed. The integration is at the level of image segments rather than pixels, and object-level image segmentation is achieved. Experiments on both synthetic and real anaglyph images demonstrate the effectiveness of the proposed image partition method for anaglyph image analysis. To the best of our knowledge, our work is for the first time to perform anaglyph image partition.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Forsyth D, Ponce J (2003) Computer vision: a modern approach. Prentice Hall, New Jersey

    Google Scholar 

  2. Zhu SC, Yuille AL (1996) Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE PAMI 18(9):884–900

    Article  Google Scholar 

  3. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE PAMI 22(8):888–905

    Article  Google Scholar 

  4. Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE TPAMI 23(11):1222–1239

    Article  Google Scholar 

  5. Delong A, Osokin A, Isack H, Boykov Y (2012) Fast approximate energy minimization with label costs. IJCV 96(1):1–27

  6. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916

    Article  Google Scholar 

  7. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181

    Article  Google Scholar 

  8. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE PAMI 24(5):603–619

    Article  Google Scholar 

  9. De A, Guo C (2014) An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing. Int J Mach Learn Cybern 5(4):543–551

    Article  Google Scholar 

  10. Ma J, Tian D, Gong M, Jiao L (2014) Fuzzy clustering with non-local information for image segmentation. Int J Mach Learn Cybern. doi:10.1007/s13042-014-0227-3

  11. Zhang C, Wang L, Yang R (2010) Semantic segmentation of urban scenes using dense depth maps. In: Daniilidis K, Maragos P, Paragios N (eds) ECCV (4), volume 6314 of Lecture Notes in Computer Science. Springer, pp 708–721

  12. Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: Fitzgibbon AW, Lazebnik S, Perona P, Sato Y, Schmid C (eds) ECCV (5), volume 7576 of Lecture Notes in Computer Science. Springer, pp 746–760

  13. Zhang Q, Liu S, An P, Zhang Z (2009) Object segmentation based on disparity estimation. In: GEC ’09: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation. ACM, New York, pp 1053–1056

  14. Chi L, Guo L, Yu L, Chen Y (2010) Object segmentation based on accurate disparity estimation. In: 2010 2nd international conference on information science and engineering (ICISE). IEEE, pp 4609–4612

  15. Gu D, Zhao Y, Yuan Y, Hu G (2012) Human segmentation based on disparity map and grabcut. In: 2012 international conference on computer vision in remote sensing (CVRS). IEEE, pp 67–71

  16. Rollmann W (1853) Notiz zur stereoskopie. Annalen der Physik 165(6):350–351

    Article  Google Scholar 

  17. Ideses I, Yaroslavsky L (2005) Three methods that improve the visual quality of color anaglyphs. J Optics A Pure Appl Optics 7:755–762

    Article  Google Scholar 

  18. Li S, Ma L, Ngi Ngan K (2013) Anaglyph image generation by matching color appearance attributes. Signal Process Image Commun 28(6):597C607

  19. Lu Z, ur Rehman S, Sikandar Lal Khan M, Li H (2013) Anaglyph 3D stereoscopic visualization of 2D video based on fundamental matrix. In: Proceedings of 2013 international conference on virtual reality and visualization (ICVRV 2013). IEEE, pp 305–308

  20. Matsuura F, Fujisawa N (2008) Anaglyph stereo visualization by the use of a single image and depth information. J Visual 11(1):79–86

    Article  Google Scholar 

  21. Zingarelli MRU, Antonio de Andrade L, Goularte R (2012) Revglyph: a technique for reverting anaglyph stereoscopic videos. In: SAC 2012, pp 1005–1011

  22. Joulin A, Kang SB (2013) Recovering stereo pairs from anaglyphs. In: CVPR. IEEE, pp 289–296

  23. Gallagher A (2010) Detecting anaglyph images with channel alignment features. In: ICIP, pp 2985–2988

  24. Hothersall D (2003) History of psychology. McGraw-Hill, New York

    Google Scholar 

  25. Kersten D, Yuille AL (2003) Bayesian models of object perception. Curr Opin Neurobiol 13:150–158

    Article  Google Scholar 

  26. Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47:7–42

    Article  MATH  Google Scholar 

  27. Klaus A, Sormann M, Karner K (2006) Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: ICPR, pp 15–18

  28. Bleyer M, Gelautz M (2005) Graph-based surface reconstruction from stereo pairs using image segmentation. SPIE Symp Electron Imaging (Videometrics VIII) 5665:288–299

    Google Scholar 

  29. Felzenszwalb PF, Huttenlocher DP (2004) Efficient belief propogation for early vision. In: CVPR, pp 261–268

  30. Ladicky L et al (2010) What, where & how many? Combining object detectors and crfs’. In: ECCV, pp 424–437

  31. Gould S, Fulton R, Koller D (2009) Decomposing a scene into geometric and semantically consistent regions. In: ICCV, pp 1–8

  32. Hoiem D, Rother C, Winn J (2007) 3D layout CRF for multi-view object class recognition and segmentation. In: CVPR, pp 1–8

  33. Shotton J, Winn J, Rother C, Criminisi A (2006) Textonboost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: ECCV, pp 1–15

  34. Ladicky L et al (2010) Joint optimisation for object class segmentation and dense stereo reconstruction. In: IJCV 100(2):122–133

Download references

Acknowledgments

The work is partially supported by the National Natural Science Foundation of China (No. 61202312), and a NSF CITeR award.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qin Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Q., Guo, G. & Liang, J. A cue integration method for anaglyph image partition. Int. J. Mach. Learn. & Cyber. 7, 983–993 (2016). https://doi.org/10.1007/s13042-014-0304-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-014-0304-7

Keywords