Abstract
In this paper, we present an algorithm for estimating disparity for images containing large textureless regions. We propose a fast and efficient region growing algorithm for estimating the stereo disparity. Though we present results on ice images, the algorithm can be easily used for other applications. We modify the first-best region growing algorithm using relaxed uniqueness constraints and matching for sub-pixel values and slant surfaces. We provide an efficient method for matching multiple windows using a linear transform. We estimate the parameters required by the algorithm automatically based on initial correspondences. Our method was tested on synthetic, benchmark and real outdoor data. We quantitatively demonstrated that our method performs well in all three cases.
This work was made possible by National Science Foundation (NSF) Office of Polar Program grants, ANT0636726 and ARC0612105.
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MV, R., Somanath, G., Kambhamettu, C., Geiger, C., Finnegan, D. (2010). Modified Region Growing for Stereo of Slant and Textureless Surfaces. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_64
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DOI: https://doi.org/10.1007/978-3-642-17289-2_64
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