2 research outputs found
Augmented Semantic Signatures of Airborne LiDAR Point Clouds for Comparison
LiDAR point clouds provide rich geometric information, which is particularly
useful for the analysis of complex scenes of urban regions. Finding structural
and semantic differences between two different three-dimensional point clouds,
say, of the same region but acquired at different time instances is an
important problem. A comparison of point clouds involves computationally
expensive registration and segmentation. We are interested in capturing the
relative differences in the geometric uncertainty and semantic content of the
point cloud without the registration process. Hence, we propose an
orientation-invariant geometric signature of the point cloud, which integrates
its probabilistic geometric and semantic classifications. We study different
properties of the geometric signature, which are an image-based encoding of
geometric uncertainty and semantic content. We explore different metrics to
determine differences between these signatures, which in turn compare point
clouds without performing point-to-point registration. Our results show that
the differences in the signatures corroborate with the geometric and semantic
differences of the point clouds.Comment: 18 pages, 6 figures, 1 tabl
Tensor Fields for Data Extraction from Chart Images: Bar Charts and Scatter Plots
Charts are an essential part of both graphicacy (graphical literacy), and
statistical literacy. As chart understanding has become increasingly relevant
in data science, automating chart analysis by processing raster images of the
charts has become a significant problem. Automated chart reading involves data
extraction and contextual understanding of the data from chart images. In this
paper, we perform the first step of determining the computational model of
chart images for data extraction for selected chart types, namely, bar charts,
and scatter plots. We demonstrate the use of positive semidefinite second-order
tensor fields as an effective model. We identify an appropriate tensor field as
the model and propose a methodology for the use of its degenerate point
extraction for data extraction from chart images. Our results show that tensor
voting is effective for data extraction from bar charts and scatter plots, and
histograms, as a special case of bar charts.Comment: 17 pages, 7 figures, 1 table, peer-reviewed and accepted for
publication in "Topological Methods in Visualization: Theory, Software and
Applications," Ingrid Hotz, Talha Bin Masood, Filip Sadlo, and Julien Tierny
(Eds.). Springer-Verla