A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A nonseparable VLSI architecture for two-dimensional discrete periodized wavelet transform
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
VLSI Implementation for One-Dimensional Multilevel Lifting-Based Wavelet Transform
IEEE Transactions on Computers
Wavelet descriptor of planar curves: theory and applications
IEEE Transactions on Image Processing
The generalized uniqueness wavelet descriptor for planar closed curves
IEEE Transactions on Image Processing
Multiresolution detection of spiculated lesions in digital mammograms
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
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Wavelet-based features with simplicity and high efficacy have been used in many pattern recognition (PR) applications. These features are usually generated from the wavelet coefficients of coarse levels (i.e., high octaves) in the discrete periodized wavelet transform (DPWT). In this paper, a new 1-D non-recursive DPWT (NRDPWT) is presented for real-time high octave decomposition. The new 1-D NRDPWT referred to as the 1-D RRO-NRDPWT can overcome the word-length-growth (WLG) effect based on two strategies, resisting error propagation and applying a reversible round-off linear transformation (RROLT) theorem. Finite precision performance analysis is also taken to study the word length suppression efficiency and the feature efficacy in breast lesion classification on ultrasonic images. For the realization of high octave decomposition, a segment accumulation algorithm (SAA) is also presented. The SAA is a new folding technique that can reduce multipliers and adders dramatically without the cost of increasing latency.