Abstract
In this paper, we proposed two window-based methods, spatial weight shiftable window and spatial weight multiple window, for correspondence problem in stereo matching. The spatial weight shiftable window is an improvement of a shiftable window method while the spatial weight multiple window is an enhancement of a multiple window method. They combine spatial weighted window for each support window, and they hence can work well in the regions of disparity discontinuity or object boundaries. The window costs in our approaches is calculated by deploying spatial weighted window for each support window, and the similarity is finally selected by a Winner-Takes-All strategy. The experimental results for the Middleburry images illustrated that the proposed algorithms outperform test local stereo algorithms.
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Tran, T.D., Nguyen, H.P., Dinh, Q.V. (2012). Stereo Matching by Fusion of Local Methods and Spatial Weighted Window. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_16
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DOI: https://doi.org/10.1007/978-3-642-35455-7_16
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