Hierarchical Segmentation of Sparse Surface Data Using Energy-Minimization Approach
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.
Key wordsSurface Reconstruction Discontinuioty Detection Multigrid Regularization
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