Publications

Abstract not provided.

Abstract not provided.

Our work in radiographic image matching has centered on the use of SURF (Speeded Up Robust Features) for feature detection, and FLANN (Fast Learning Artificial Neural Network) for feature matching. We discovered that while the SURF process does return information on location, scale, and rotation for each detected feature, they are not essential for image matching. The nature of the remaining feature detection data does not appear to contain any useful information in terms of reconstructing a useful portion of an image, and therefore is not amenable to reconstructing the original image. This led us to wonder if, in fact, we had discovered an irreversible process; the original image could not be constructed from the remaining feature data. Additional detail on the derivation of the image processing and matching algorithms and the irreversibility hypothesis are available in the final SAND Report documenting our previous LDRD work (SAND2015-9665 “Processing Radiation Images Behind an Information Barrier for Automatic Warhead Authentication” Little, Wilson, Weber and Novick, 2015).

In this paper, we consider the problem of denoising and classification of SONAR signals observed under compositional noise, i.e., they have been warped randomly along the x-axis. The traditional techniques do not account for such noise and, consequently, cannot provide a robust classification of signals. We apply a recent framework that: 1) uses a distance-based objective function for data alignment and noise reduction; and 2) leads to warping-invariant distances between signals for robust clustering and classification. We use this framework to introduce two distances that can be used for signal classification: a) a y-distance, which is the distance between the aligned signals; and b) an x-distance that measures the amount of warping needed to align the signals. We focus on the task of clustering and classifying objects, using acoustic spectrum (acoustic color), which is complicated by the uncertainties in aspect angles at data collections. Small changes in the aspect angles corrupt signals in a way that amounts to compositional noise. As a result, we demonstrate the use of the developed metrics in classification of acoustic color data and highlight improvements in signal classification over current methods.