Abstract
Recent progresses in stereo research imply that performance of the disparity estimation depends on the discontinuity localization in the disparity space which is generally predicated on discontinuities in the image intensities. However, these approaches have known limitations at highly textured and occluded regions. In this paper, we propose to employ a layered representation of the scene as an approximation of the scene structure. The layered representation of the scenes was obtained by using partially focused image set of the scene. Although self occlusions are still present in real aperture imaging systems, our approach does not suffer from the occlusion problems as much as stereo and focus/defocus based methods. Our disparity estimation method is based on synchronously optimized two interdependent processes which are regularized with a nonlinear diffusion operator. The amount of diffusion between the neighbors is adjusted adaptively according to information in the layered scene representation and temporal positions of the processes. The system is initialization insensitive and very robust against local minima. In addition, it accurately handles the depth discontinuities. The performance of the presented method has been verified through experiments on real and synthetic scenes.
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References
Tappen, M.F., Freeman, W.T.: Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters. In: International Conference on Computer Vision, pp. 900–907 (2003)
Yoon, K.-J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 650–656 (2006)
Zhang, Y., Kambhamettu, C.: Stereo matching with segmentation-based cooperation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. Part II. LNCS, vol. 2351, pp. 556–571. Springer, Heidelberg (2002)
Hong, L., Chen, G.: Segment-based stereo matching using graph cuts. In: IEEE Computer Vision and Pattern Recognition or CVPR, pp. I. 74–I. 81 (2004)
Alvarez, L., Deriche, R., Sanchez, J., Weickert, J.: Dense disparity map estimation respecting image discontinuities: A pde and scale-space based approach. Journal of Visual Communication and Image Representation 13(1/2), 3–21 (2002)
Raskar, R., Tan, K.H., Feris, R., Yu, J., Turk, M.: Non-photorealistic camera: depth edge detection and stylized rendering using multi-flash imaging. ACM Trans. Graph. 23(3), 679–688 (2004)
Zickler, T.E., Belhumeur, P.N., Kriegman, D.J.: Helmholtz stereopsis: Exploiting reciprocity for surface reconstruction. Int. J. Comput. Vision 49(2-3), 215–227 (2002)
Pentland, A.P.: A new sense for depth of field. IEEE Trans. Pattern Anal. Mach. Intell. 9(4), 523–531 (1987)
Schechner, Y.Y., Kiryati, N.: Depth from defocus vs. stereo: How different really are they? Int. J. Comput. Vision 39(2), 141–162 (2000)
Aydin, T., Akgul, Y.: Stereo depth estimation using synchronous optimization with segment based regularization. Technical report, Gebze Institude of Technology, Kocaeli, Turkey (2008)
Ahuja, N., Abbott, A.L.: Active stereo: Integrating disparity, vergence, focus, aperture and calibration for surface estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(10), 1007–1029 (1993)
Nayar, S.K., Nakagawa, Y.: Shape from focus. PAMI 16(8), 824–831 (1994)
Asada, N., Fujiwara, H., Matsuyama, T.: Seeing behind the scene: analysis of photometric properties of occluding edges by the reversed projection blurring model. In: IEEE International Conference on Computer Vision, p. 150 (1995)
Aydin, T., Akgul, Y.S.: A new adaptive focus measure for shape from focus. In: BMVC 2008 (2008)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–846 (1998)
Sakurai, R.: Irisfilter (2004), http://www.reiji.net/
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47(1-3), 7–42 (2002)
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Aydin, T., Akgul, Y.S. (2009). A Stereo Depth Recovery Method Using Layered Representation of the Scene. 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_33
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DOI: https://doi.org/10.1007/978-3-642-03798-6_33
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