Statistical analysis and classification of acoustic color functions

J. Derek Tucker; Anuj Srivastava

doi:10.1117/12.884537

23 May 2011 Statistical analysis and classification of acoustic color functions

J. Derek Tucker, Anuj Srivastava

Proceedings Volume 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI; 80170O (2011) https://doi.org/10.1117/12.884537
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

J. Derek Tucker and Anuj Srivastava "Statistical analysis and classification of acoustic color functions", Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170O (23 May 2011); https://doi.org/10.1117/12.884537

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