DocumentCode :
2539763
Title :
Using three-dimensional features to improve terrain classification
Author :
Wang, Xiaoguang ; Stolle, Frank ; Schultz, Howard ; Riseman, Edward M. ; Hanson, Allen R.
Author_Institution :
Dept. of Comput. Sci., Massachusetts Univ., Amherst, MA, USA
fYear :
1997
fDate :
17-19 Jun 1997
Firstpage :
915
Lastpage :
920
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
Keywords :
computer vision; geophysical techniques; image texture; 3-D world feature; classification accuracy; gray-level variation; spatial distributions; terrain classification; texture analysis; three-dimensional features; Cameras; Feature extraction; Image analysis; Image texture; Image texture analysis; Optical sensors; Rough surfaces; Signal processing algorithms; Surface roughness; Surface texture;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
Conference_Location :
San Juan
ISSN :
1063-6919
Print_ISBN :
0-8186-7822-4
Type :
conf
DOI :
10.1109/CVPR.1997.609437
Filename :
609437
Link To Document :
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