Abstract
This work aims at defining a new method for matching correspondences in stereoscopic image analysis. The salient aspects of the method are -an explicit representation of occlusions driving the overall matching process and the use of neural adaptive technique in disparity computation. In particular, based on the taxonomy proposed by Scharstein and Szelinsky, the dense stereo matching process has been divided into three tasks: matching cost computation, aggregation of local evidence and computation of disparity values. Within the second phase a new strategy has been introduced in an attempt to improve reliability in computing disparity. An experiment was conducted to evaluate the solutions proposed. The experiment is based on an analysis of test images including data with a ground truth disparity map.
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References
Barnard, S.T., Fischler, M.A.: Computational Stereo. ACM Computing Surveys 14(4), 553–572 (1982)
Barnard, T., Thompson, W.B.: Disparity Analysis of Images. IEEE Trans. PAMI, 333–340 (1980)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Bobik, A.F., Intille, S.S.: Large occlusion stereo. International Journal on Computer Vision 33, 181–200 (1999)
Cox, J.I., Higonani, S.L., Rao, S.P., Maggs, B.M.: A Maximum Likelihoods Stereo Algorithm. Computer Vision and Image Understanding 63, 542–567 (1996)
Dhond, U.R., Aggarwal, J.K.: Structure from Stereo – a review. IEEE Trans. On Systems, Man, and Cybernetics 19, 1489–1510 (1989)
Hannah, M.J.: A system for digital stereo image matching. Photogrammetric Engineering and Remote Sensing 55, 1765–1770 (1989)
McMillan, L., Bishop, G.: Plenoptic modelling:An image-based rendering system. In: SIG-GRAPH 1995. Computer Graphics, pp. 39–46 (1995)
Kanade, T., Okutomi, M.: A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment. IEEE Trans. on PAMI 16(9), 920–932 (1994)
Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, MA (1989)
Rumelhart, H., Hinton, G.E., Williams, R.J.: Learning Internal Representation by Error Propagation. In: Rumelhart, H., McClelland, J.L. (eds.) Parallel Distributed Processing, pp. 318–362. MIT Press, Cambridge, MA (1986)
Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision 47, 7–42 (2002)
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© 2007 Springer-Verlag Berlin Heidelberg
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Gallo, I., Binaghi, E. (2007). Dense Stereo Matching with Growing Aggregation and Neural Learning. In: Braz, J., Ranchordas, A., Araújo, H., Jorge, J. (eds) Advances in Computer Graphics and Computer Vision. Communications in Computer and Information Science, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75274-5_24
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DOI: https://doi.org/10.1007/978-3-540-75274-5_24
Publisher Name: Springer, Berlin, Heidelberg
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