Scale and translation invariant shape and signal classification and detection

William J. Williams

doi:10.1117/12.513902

Abstract

Highly sophisticated methods for detection and classification of signals and images are available. However, most of these methods are not robust to nonstationary variations such as imposed by Doppler effects or other forms of warping. Fourier methods handle time-shift or frequency shift variations in signals or spatial shifts in images. A number of methods have been developed to overcome these problems. In this paper we discuss some specific approaches that have been motivated by time-frequency analysis. Methodologies developed for images can often be profitably used for time-frequency analysis as well, since these representations are essentially images. The scale transform introduced by Cohen can join Fourier transforms in providing robust representations. Scale changes are common in many signal and image scenarios. We call the representation which results from appropriate transformations of the object of interest the Scale and Translation Invariant Representation or STIR. The STIR method is summarized and results from machine diagnosis, radar, marine mammal sounds, TMJ sounds, speech and word spotting are discussed. Some of the limitations and variations of the method are discussed to provide a rationale for selection of particular elements of the method.

Citation Download Citation

William J. Williams, William J. Williams, "Scale and translation invariant shape and signal classification and detection", Proc. SPIE 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII, (24 December 2003); doi: 10.1117/12.513902; https://doi.org/10.1117/12.513902

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