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Depth from Moving Apertures

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Information Sciences and Systems 2013

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 264))

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Abstract

Two new focus measure operators for Shape From Focus to estimate the three-dimensional shape of a surface are proposed in this paper. The images are formed by a camera using moving apertures. The well-focused image pixels are identified by frequency analysis or matching with the all-focused image of the scene. Frequency analysis involves the usage of classical focus measure operators and summing up for each aperture to find the focus quality to be maximized. The lesser method uses the match-points and error ratios of any matching algorithm used within an all-focused image region and all same-focus-different-aperture images to find the total displacement. The inverse of total displacement can be used as a focus quality measure. The experiments on real images show that the introduced ideas work effectively and efficiently.

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References

  1. Aydin T, Akgül YS (2008) A new adaptive focus measure for shape from focus. British machine vision conference, Leeds (September)

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  2. Nair HN, Stewart CV (1992) Robust focus ranging. Computer Vision and Pattern Recognition, pp 309–314

    Google Scholar 

  3. Nayar SK, Nakagawa Y (August 1994) Shape from focus. Pattern Anal Mach Intell 16(8):824–831

    Article  Google Scholar 

  4. Pratt WK (1978) Digital image processing. Wiley, New York

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  5. Scharstein D, Szeliski R (2003) High-accuracy stereo depth maps using structured light. Computer Vision and Pattern Recognition, p. 195–202, Wisconsin

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  6. Schechner YY, Kiryati N (2000) Depth from defocus versus stereo: how different really are they? Int J Comput Vis 39(2):141–162

    Article  MATH  Google Scholar 

  7. Subbaro M, Surya G (1994) Depth from defocus: a spatial domain approach. Int J Comput Vis 13(3):271–294

    Article  Google Scholar 

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Acknowledgments

This work was supported by TUBITAK Career Project 105E097.

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Correspondence to Mahmut Salih Sayar or Yusuf Sinan Akgül .

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© 2013 Springer International Publishing Switzerland

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Sayar , M.S., Akgül, Y.S. (2013). Depth from Moving Apertures. In: Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2013. Lecture Notes in Electrical Engineering, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-01604-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-01604-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01603-0

  • Online ISBN: 978-3-319-01604-7

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