Abstract
Shape from focus (SFF) is a widely used technique for determining the 3D structure of textured microscopic objects. However, SFF output depends critically on the number of observations used and the focus measure operator adopted. In this paper, we propose a new SFF method that can provide rich structure information given limited number of observations. We observe that depth is non-linearly related to the observations and pose the shape estimation as a minimization problem within a Maximum A Posteriori (MAP) - Markov Random Field (MRF) framework. We incorporate a discontinuity-adaptive MRF prior for the underlying structure. The resulting cost function is non-convex in nature which we minimize using Graduated non-convexity algorithm. When tested on synthetic as well as real objects, the results obtained are quite impressive.
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References
Favaro, P., Soatto, S.: 3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion Blur. Springer, Heidelberg (2006)
Nayar, S.: Shape from focus system. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 1992, pp. 302–308 (1992)
Chaudhuri, S., Rajagopalan, A.: Depth from Defocus: A Real Aperture Imaging Approach. Springer, Heidelberg (1998)
Favaro, P., Soatto, S.: A geometric approach to shape from defocus. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 406–417 (2005)
Favaro, P., Soatto, S., Burger, M., Osher, S.: Shape from defocus via diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 518–531 (2008)
Subbarao, M., Choi, T.: Accurate recovery of three-dimensional shape from image focus. IEEE Trans. Pattern Anal. Mach. Intell. 17(3), 266–274 (1995)
Scharstein, D., Pal, C.: Learning conditional random fields for stereo. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2007, pp. 1–8 (2007)
Brodatz, P.: Textures; a photographic album for artists and designers. Dover Publications, New York (1966)
Hasinoff, S.W., Kutulakos, K.N.: Confocal stereo. Int’l J. Computer Vision 81(1), 82–104 (2009)
Sorel, M.: Multichannel blind restoration of images with space-variant degradations. PhD thesis, Charles Univ., Prague, Czech Republic, Department of Software Engineering Faculty of Mathematics and Physics (March 2007)
Subrahmanyam, G., Rajagopalan, A., Aravind, R.: Importance sampling kalman filter for image estimation. IEEE Signal Process. Lett. 14(7), 453–456 (2007)
Pentland, A.P.: A new sense for depth of field. IEEE Trans. Pattern Anal. Mach. Intell. 9(4), 523–531 (1987)
Born, M., Wolf, E.: Principles of Optics. Pergamon Press, Oxford (1993)
Li, S.Z.: Markov random field modeling in computer vision. Springer, London (1995)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Blake, A., Zisserman, A.: Visual reconstruction. MIT Press, Cambridge (1987)
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Ramnath, K., Rajagopalan, A.N. (2009). Discontinuity-Adaptive Shape from Focus Using a Non-convex Prior. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_19
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DOI: https://doi.org/10.1007/978-3-642-03798-6_19
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