Context-driven automated target detection in 3D data

By K. West, Brian N. Webb, James R. Lersch, S. Pothier, J. Triscari, A. E. Iverson. 2004

103 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Liangliang Nan
2. Hugo Ledoux
3. B. Boom
4. Weixiao Gao
5. Ulrich Thoennessen
6. Nicolas David
7. Clément Mallet

In Environmental Engineering

1. Hugo Ledoux
2. Liangliang Nan
3. B. Boom
4. Weixiao Gao
5. Clément Mallet
6. Francesco Pirotti
7. Suya You
8. Jing Huang

Citations in the corpus

• DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS (2012, background): “Several indicators have already been proposed (West et al., 2004; Toshev et al., 2010), and the following ones have been selected (Figure 2) to describe the linear (a1D), planar (a2D), and scatter (a3D) behaviors within VrP : a1D = σ1 − σ2 µ , a2D = σ2 − σ3 µ , a3D = σ3 µ , where µ is the…”
• Segmentation of tree regions using data of a full-waveform laser (2007, background): “(5) For object classification, West et al. (2004) uses the following features which depend on the eigenvalues: structure tensor omnivariance 3 3 1 i i λ = = ∏ , (6) structure tensor planarity 2 3 1 λ λ λ −= .”
• SUM: A Benchmark Dataset of Semantic Urban Meshes (2021, background): “Note that many eigen-based features have been studied in literature [39, 40, 41], and some of them were designed for and tested on LiDAR point clouds.”