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28 July 1997 Wavelet-based rotationally invariant target classification
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Abstract
In this paper, a novel approach to feature extraction for rotationally invariant object classification is proposed based directly on a discrete wavelet transformation. This form of feature extraction is equivalent to retaining information features while eliminating redundant features from images, which is a critical property when analyzing large, high dimensional images. Usually, researchers have resorted to a data pre-processing method to reduce the size of the feature space prior to classification. The proposed method employs statistical features extracted directly from the wavelet coefficients generated from a three-level subband decomposition system using a set of orthogonal and regular Quadrature Mirror Filters. This algorithm has two desirable properties: (1) It reduces the number of dimensions of the feature space necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; (2) Regardless of the target orientation, the algorithm can perform classification with low error rates. Furthermore, the filters used have performed well in the image compression regime, but they have not been applied to applications in target classification which will be demonstrated in this paper. The results of several classification experiments on variously oriented samples of the visible wavelength targets will be presented.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Victoria T. Franques and David Alan Kerr "Wavelet-based rotationally invariant target classification", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); https://doi.org/10.1117/12.280791
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