A statistical analysis of 3D structure tensor features generated from LADAR imagery

Miguel Ordaz; Estille Whittenberger; Donald Waagen; Donald Hulsey

doi:10.1117/12.673389

18 May 2006 A statistical analysis of 3D structure tensor features generated from LADAR imagery

Miguel Ordaz, Estille Whittenberger, Donald Waagen, Donald Hulsey

Proceedings Volume 6234, Automatic Target Recognition XVI; 623408 (2006) https://doi.org/10.1117/12.673389
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

Extraction and efficient representation of informative structure from data is the goal of pattern recognition. Efficient and effective parametric and nonparametric representations for capturing the geometry of three-dimensional objects are an area of current research. Tang and Medioni have proposed tensor representations for characterization and reconstruction of surfaces. 3-D structure tensors are extracted by mapping surface geometries using a rank-2 covariant tensor. Distributional differences between representations of objects of interest can (theoretically) be used for target matching and identification. This paper analyzes the statistical distributions of tensor representation extracted from 3-D LADAR imagery and quantifies a measure of divergence between images of three vehicles as a function of tensor feature support size.

Citation Download Citation

Miguel Ordaz, Estille Whittenberger, Donald Waagen, and Donald Hulsey "A statistical analysis of 3D structure tensor features generated from LADAR imagery", Proc. SPIE 6234, Automatic Target Recognition XVI, 623408 (18 May 2006); https://doi.org/10.1117/12.673389

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