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Transparent Layers

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Computer Vision
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Synonyms

Transparency

Related Concepts

Polarized Light in Computer Vision; Transparency and Translucency

Definition

In computer vision, the term transparent layers refers to physical objects located at different depths from an imaging device, in such a configuration that irradiance coming from all these objects contributes to non-negligible portions of the final intensities of the same pixels, giving the perception of transparency.

Solving a transparent layer problem entails estimating a subset of the following values: intensity/color of each layer, depth of each layer, geometric parameters related to the camera pose and scene structure, and optical properties of the transparent layers.

Background

Phenomena of transparent layers can appear in many imaging processes. The most typical transparent layers occur due to transparency. For instance, objects seen through a textured and transparent material, for example, a tinted glass window or a thin curtain, where the layers include a front...

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References

  1. Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47(1):7–42

    Article  Google Scholar 

  2. Levin A, Rav-Acha A, Lischinski D (2008) Spectral matting. IEEE TPAMI 30(10):1699–1712

    Article  Google Scholar 

  3. . Shizawa M, Mase K (1990) Simultaneous multiple optical flow estimation. In: ICPR'90, Atlantic City. IEEE Computer Society

    Google Scholar 

  4. Bergen JR, Burt PJ, Hingorani R, Peleg S (1992) A three-frame algorithm for estimating two-component image motion. IEEE TPAMI 14(9):886–896

    Article  Google Scholar 

  5. Irani M, Rousso B, Peleg S (1992) Detecting and tracking multiple moving objects using temporal integration. In: European conference on computer vision (ECCV'92), Santa Margherita Liguere. Springer, pp 282–287

    Chapter  Google Scholar 

  6. Ju SX, Black MJ, Jepson AD (1996) Skin and bones: multi-layer, locally affine, optical flow and regularization with transparency. In: IEEE conference on computer vision pattern recognition (CVPR'96), San Francisco. IEEE, pp 307–314

    Chapter  Google Scholar 

  7. Szeliski R, Golland P (1999) Stereo matching with transparency and matting. IJCV 32(1):45–61

    Article  Google Scholar 

  8. Szeliski R, Avidan S, Anandan P (2000) Layer extraction from multiple images containing reflections and transparency. In: IEEE conference on computer vision pattern recognition (CVPR'2000), vol 1, Hilton Head Island. IEEE Computer Society, pp 246–253

    Google Scholar 

  9. Tsin Y, Kang SB, Szeliski R (2006) Stereo matching with linear superposition of layers. IEEE TPAMI 28(2):290–301

    Article  Google Scholar 

  10. Sun J, Jia J, Tang CK, Shum HY (2004) Poisson matting. In: SIGGRAPH 2004, Los Angeles, SIGGRAPH: ACM

    Google Scholar 

  11. Wexler Y, Fitzgibbon A, Zisserman A (2002) Bayesian estimation of layers from multiple images. In: European conference on computer vision (ECCV'02), Volume 2352 of Lecture Notes in Computer Science, Copenhagen. Springer, pp 487–501

    Google Scholar 

  12. Sarel B, Irani M (2004) Separating transparent layers through layer information exchange. In: European conference on computer vision (ECCV'04), Prague. Springer

    Google Scholar 

  13. Chen Y, Chang TC, Zhou C, Fang T (2009) Gradient domain layer separation under independent motion. In: ICCV 2009, Kyoto. IEEE

    Google Scholar 

  14. Schechner Y, Kiryati N, Basri R (1998) Separation of transparent layers using focus. In: ICCV'98, Bombay. IEEE Computer Society, pp 1061–1066

    Google Scholar 

  15. Schechner Y, Shamir J, Kiryati N (1999) Polarization-based decorrelation of transparent layers: the inclination angle of an invisible surface. In: ICCV'99, Kerkyra. IEEE Computer Society, pp 814–819

    Google Scholar 

  16. Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. IJCV 48(3):233–254

    Article  Google Scholar 

  17. He K, Sun J, Tang X (2009) Single image haze removal using dark channel prior. In: IEEE conference on computer vision pattern recognition (CVPR'09), Miami. IEEE

    Google Scholar 

  18. Farid H, Adelson E (1999) Separating reflections and lighting using independent components analysis. In: IEEE conference on computer vision pattern recognition (CVPR'99), vol 1., Fort Collins. IEEE Computer Society, pp 262–267

    Google Scholar 

  19. Schechner Y, Kiryati N, Shamir J (2000) Blind recovery of transparent and semireflected scenes. In: IEEE conference on computer vision pattern recognition (CVPR'00), vol 2, Hilton Head Island, IEEE Computer Society, pp 38–43

    Google Scholar 

  20. . Bronstein A, Bronstein M, Zibulevsky M, Zeevi Y (2003) Blind separation of reflections usnig sparse ICA. In: 4th international symposium on independent component analysis and blind signal separation (ICA'03), Nara http://www.kecl.ntt.co.jp/icl/signal/ica2003/

  21. Levin A, Zomet A, Weiss Y (2002) Learning to perceive transparency from the statistics of natural scenes. In: NIPS'02, Vancouver. MIT

    Google Scholar 

  22. Wang JYA, Adelson EH (1994) Representing moving images with layers. IEEE Trans Image Process 3(5): 625–638

    Article  Google Scholar 

  23. Darrell TJ, Pentland AP (1995) Cooperative robust estimation using layers of support. IEEE TPAMI 17(5):474–487

    Article  Google Scholar 

  24. . Shizawa M, Mase K (1991) Principle of superposition: a common computational framework for analysis of multiple motion. IEEE workshop on visual motion, Princeton. IEEE, pp 164–172

    Google Scholar 

  25. Swaminathan R, Kang S, Szeliski R, Criminisi A, Nayar S (2002) On the motion and appearance of specularities in image sequences. In: European conference on computer vision (ECCV'02), Volume 2350 of lecture notes in computer science, Copenhagen. Springer, pp 508–523

    Google Scholar 

  26. Levin A, Weiss Y, Durand F, Freeman WT (2009) Understanding and evaluating blind deconvolution algorithms. In: IEEE conference on computer vision pattern recognition (CVPR'09), Miami. IEEE

    Google Scholar 

  27. . Zhu Y, Prummer S, Chen T, Ostermeier M, Comaniciu D (2009) Coronary DSA: enhancing coronary tree visibility through discriminative learning and robust motion estimation. In: SPIE medical imaging (2009). SPIE Lake Buena Vista (Orlando Area), Florida. http://spie.org/x33859.xml

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Tsin, Y. (2014). Transparent Layers. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_561

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