Direct Depth Recovery from Motion Blur Caused by Random Camera Rotations Imitating Fixational Eye Movements

Norio Tagawa, Shoei Koizumi, Kan Okubo

Abstract

It has been reported that small involuntary vibrations of a human eyeball for fixation called ”fixational eye movements” play a role of image analysis, for example contrast enhancement and edge detection. This mechanism can be interpreted as an instance of stochastic resonance, which is inspired by biology, more specifically by neuron dynamics. A depth recovery method has been proposed, which uses many successive image pairs generated by random camera rotations imitating fixational eye movements. This method, however, is not adequate for images having fine texture details because of an aliasing problem. To overcome this problem, we propose a new integral formed method for recovering depth, which uses motion blur caused by the same camera motions, i.e. many random small camera rotations. As an algorithm, we examine a method directly recovering depth without computing a blur function. To confirm the feasibility of our scheme, we perform simulations using artificial images.

References

  1. Bruhn, A. and Weickert, J. (2005). Locas/kanade meets horn/schunk: combining local and global optic flow methods. Int. J. Comput. Vision, 61(3):211-231.
  2. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data. J. Roy. Statist. Soc. B, 39:1-38.
  3. Gammaitoni, L., Hanggi, P., Jung, P., and Marchesoni, F. (1998). Stochastic resonance.
  4. Greenwood, P. E., Ward, L. M., and Wefelmeyer, W. (1999). Statistical analysis of stochastic resonance in a simple setting. Physical Rev. E, 60:4687-4696.
  5. Hongler, M.-O., de Meneses, Y. L., Beyeler, A., and Jacot, J. (2003). The resonant retina: exploiting vibration noise to optimally detect edges in an image. IEEE Trans. Pattern Anal. Machine Intell., 25(9):1051- 1062.
  6. Horn, B. P. and Schunk, B. (1981). Determining optical flow. Artif. Intell., 17:185-203.
  7. Jazwinski, A. (1970). Stochastic processes and filtering theory. Academic Press.
  8. Martinez-Conde, S., Macknik, S. L., and Hubel, D. (2004). The role of fixational eye movements in visual perception. Nature Reviews, 5:229-240.
  9. Nayar, S. K. and Nakagawa, Y. (1994). Shape from focus. IEEE Trans. Pattern Anal. Machine Intell., 16(8):824- 831.
  10. Oliver, C. and Quegan, S. (1998). Understanding synthetic aperture radar images. Artech House, London.
  11. Paramanand, C. and Rajagopalan, A. N. (2012). Depth from motion and optical blur with unscented kalman filter. IEEE Trans. Image Processing, 21(5):2798-2811.
  12. Poggio, T., Torre, V., and Koch, C. (1985). Computational vision and regularization theory. Nature, 317:314- 319.
  13. Propokopowicz, P. and Cooper, P. (1995). The dynamic retina. Int'l J. Computer Vision., 16:191-204.
  14. Simoncelli, E. P. (1999). Bayesian multi-scale differential optical flow. In Handbook of Computer Vision and Applications, pages 397-422. Academic Press.
  15. Sorel, M. and Flusser, J. (2008). Space-variant restoration of images degraded by camera motion blur. IEEE Trans. Image Processing, 17(2):105-116.
  16. Stemmler, M. (1996). A single spike suffices: the simplest form of stochastic resonance in model neuron. Network: Computations in Neural Systems, 61(7):687- 716.
  17. Tagawa, N. (2010). Depth perception model based on fixational eye movements using byesian statistical inference. In proc. ICPR2010, pages 1662-1665.
  18. Tagawa, N., Kawaguchi, J., Naganuma, S., and Okubo, K. (2008). Direct 3-d shape recovery from image sequence based on multi-scale bayesian network. In proc. ICPR08, page CD.
  19. Tagawa, N. and Naganuma, S. (2009). Pattern Recognition, Structure and motion from image sequence based on multi-scale Bayesian network. In-Tech, Croatia.
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Paper Citation


in Harvard Style

Tagawa N., Koizumi S. and Okubo K. (2013). Direct Depth Recovery from Motion Blur Caused by Random Camera Rotations Imitating Fixational Eye Movements . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 177-186. DOI: 10.5220/0004304001770186


in Bibtex Style

@conference{visapp13,
author={Norio Tagawa and Shoei Koizumi and Kan Okubo},
title={Direct Depth Recovery from Motion Blur Caused by Random Camera Rotations Imitating Fixational Eye Movements},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={177-186},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004304001770186},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Direct Depth Recovery from Motion Blur Caused by Random Camera Rotations Imitating Fixational Eye Movements
SN - 978-989-8565-48-8
AU - Tagawa N.
AU - Koizumi S.
AU - Okubo K.
PY - 2013
SP - 177
EP - 186
DO - 10.5220/0004304001770186