Abstract
The main objective for this research is to develop an algorithm that produces a dense representation of a surface from a sparse set of observations and facilitates preliminary labeling of discontinuities in the surface. The solution to these issues is of a great interest to the new trends and applications in digital photogrammetry, particularly for large-scale urban imagery.
This study adopts the approach of a concurrent interpolation of the surface and detection of its discontinuities by the weak membrane. The solution was achieved through developing a multigrid implementation of the Graduate Non-Convexity (GNC) algorithm. The conducted experiments proved that the developed method is adequate and applicable for dealing with large-scale images of urban areas as it was successful in producing a realistic surface representation and fulfilling other set criteria.
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Al-Tahir, R. (2005). Hierarchical Segmentation of Sparse Surface Data Using Energy-Minimization Approach. In: Saeed, K., Pejaś, J. (eds) Information Processing and Security Systems. Springer, Boston, MA. https://doi.org/10.1007/0-387-26325-X_2
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DOI: https://doi.org/10.1007/0-387-26325-X_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-25091-5
Online ISBN: 978-0-387-26325-0
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