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IEICE Transactions on Information and Systems 2008 E91-D(11):2666-2674; doi:10.1093/ietisy/e91-d.11.2666
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Copyright © 2008 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- VLSI Systems

Non-recursive Discrete Periodized Wavelet Transform Using Segment Accumulation Algorithm and Reversible Round-Off Approach

Chin-Feng TSAI1, Huan-Sheng WANG2, King-Chu HUNG2 and Shih-Chang HSIA2

1 The author is with Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, Taiwan, R.O.C., 2 The authors are with Department of Computer and Communication Engineering, National Kaohsiung First University of Science and Technology, Taiwan, R.O.C. E-mail: kchung{at}ccms.nkfust.edu.tw


  Abstract

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.

Key Words: non-recursive wavelet transform, segment accumulation algorithm (SAA), feature extractor

Manuscript received December 20, 2007. Manuscript revised June 11, 2008.


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