Abstract
In this work, we perform three-dimensional scene recovery from image data capturing railway transportation corridors. Typical three-dimensional scene recovery methods initialise recovered feature positions by searching for correspondences between image frames. We intend to take advantage of a relationship between image data and recovered scene data to reduce the search space traversed when performing such correspondence matching. We build multi-dimensional Gaussian models of recurrent visual features associated with distributions representing recovery results from our own dense planar recovery method. Results show that such a scheme decreases the number of checks made per feature to 6% of a comparable exhaustive method, whilst unaffecting accuracy. Further, the proposed method performs competitively when compared with other methods presented in literature.
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Warsop, T., Singh, S. (2011). Unsupervised Learning for Improving Efficiency of Dense Three-Dimensional Scene Recovery in Corridor Mapping. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_37
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DOI: https://doi.org/10.1007/978-3-642-21227-7_37
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