50 research outputs found
Possible application of elementary geochemical landscapes map for mapping heavy metals and metalloids content in urban soils
Mapping heavy metals and metalloids (HMM) content in urban soils is necessary for the public health risk assessment. The sampling step is usually large in comparison with typical variability intervals due to the complexity and expensiveness of analysis. This paper considers an attempt to map HMM concentration coefficients based on landscape-geochemical positions (LGP). The case study area is Darkhan, Mongolia, a large industrial and transport hub. 126 soil samples were taken for analysis of contaminants As, Cd, Cr, Cu, Pb, Sb, W; the distance between sampling points was 500–700 m. For each point, the concentration coefficient (DF) of each pollutant was calculated. The LGP map was derived from SRTM digital elevation model, with supplement of hydrographic network data. The final maps of the concentration coefficients were created using areal interpolation technique with the Voronoi diagram of sampling points as an input data and the LGP polygons as a target dataset. The relatively low sampling points density, as well as the relatively large DEM cell size limit the possibility to harmonize datasets. This leads to the noticeable difference between the parameter distribution obtained from areal interpolation and the distribution obtained from deterministic method. Besides, some resulting features should be considered as interpolation artifacts. Nevertheless, the potential suitability of LGP maps as a basis for mapping pollution of urban areas is shown
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view
images is a fundamental yet active research area in computer vision. Despite
the steady progress in multi-view stereo reconstruction, most existing methods
are still limited in recovering fine-scale details and sharp features while
suppressing noises, and may fail in reconstructing regions with few textures.
To address these limitations, this paper presents a Detail-preserving and
Content-aware Variational (DCV) multi-view stereo method, which reconstructs
the 3D surface by alternating between reprojection error minimization and mesh
denoising. In reprojection error minimization, we propose a novel inter-image
similarity measure, which is effective to preserve fine-scale details of the
reconstructed surface and builds a connection between guided image filtering
and image registration. In mesh denoising, we propose a content-aware
-minimization algorithm by adaptively estimating the value and
regularization parameters based on the current input. It is much more promising
in suppressing noise while preserving sharp features than conventional
isotropic mesh smoothing. Experimental results on benchmark datasets
demonstrate that our DCV method is capable of recovering more surface details,
and obtains cleaner and more accurate reconstructions than state-of-the-art
methods. In particular, our method achieves the best results among all
published methods on the Middlebury dino ring and dino sparse ring datasets in
terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image
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