ML Anthology

Using Three-Dimensional Features to Improve Terrain Classification

Xiaoguang Wang, Frank Stolle, Howard J. Schultz, Edward M. Riseman, Allen R. Hanson
CVPR 1997 pp. 915-920
doi:10.1109/CVPR.1997.609437 /cvpr/1997/wang1997cvpr-using/

Abstract

Texture has long been regarded as spatial distributions of gray-level variation, and texture analysis has generally been confined to the 2-D image domain. Introducing the concept of "3-D world feature", this paper considers texture as a function of 3-D structures and proposes a set of "3-D textural features". The proposed 3-D features appear to have a great potential in terrain classification. Experiments have been carried out to compare the 3-D features with a popular traditional 2-D feature set. The results show that the 3-D features significantly outperform the 2-D features in terms of classification accuracy.

CVPR Semantic Scholar

Cite

Text

Wang et al. "Using Three-Dimensional Features to Improve Terrain Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997. doi:10.1109/CVPR.1997.609437

Markdown

[Wang et al. "Using Three-Dimensional Features to Improve Terrain Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997.](https://mlanthology.org/cvpr/1997/wang1997cvpr-using/) doi:10.1109/CVPR.1997.609437

BibTeX

@inproceedings{wang1997cvpr-using,
  title     = {{Using Three-Dimensional Features to Improve Terrain Classification}},
  author    = {Wang, Xiaoguang and Stolle, Frank and Schultz, Howard J. and Riseman, Edward M. and Hanson, Allen R.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1997},
  pages     = {915-920},
  doi       = {10.1109/CVPR.1997.609437},
  url       = {https://mlanthology.org/cvpr/1997/wang1997cvpr-using/}
}