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Proceedings Paper

Statistical analysis and classification of acoustic color functions
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Paper Abstract

In this paper we present a method for clustering and classification of acoustic color data based on statistical analysis of functions using square-root velocity functions (SVRF). The convenience of the SVRF is that it transforms the Fisher-Rao metric into the standard L2 metric. As a result, a formal distance can be calculated using geodesic paths. Moreover, this method allows optimal deformations between acoustic color data to be computed for any two targets allowing for robustness to measurement error. Using the SVRF formulation statistical models can then be constructed using principal component analysis to model the functional variation of acoustic color data. Empirical results demonstrate the utility of functional data analysis for improving performance results in pattern recognition using acoustic color data.

Paper Details

Date Published: 23 May 2011
PDF: 10 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170O (23 May 2011); doi: 10.1117/12.884537
Show Author Affiliations
J. Derek Tucker, Naval Surface Warfare Ctr. (United States)
Anuj Srivastava, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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