IEICE TRANSACTIONS on Information
Non-recursive Discrete Periodized Wavelet Transform Using Segment Accumulation Algorithm and Reversible Round-Off Approach
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.11 pp.2666-2674
- Publication Date
- 2008/11/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.11.2666
- Type of Manuscript
- PAPER
- Category
- VLSI Systems
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Keyword
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Chin-Feng TSAI, Huan-Sheng WANG, King-Chu HUNG, Shih-Chang HSIA, "Non-recursive Discrete Periodized Wavelet Transform Using Segment Accumulation Algorithm and Reversible Round-Off Approach" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 11, pp. 2666-2674, November 2008, doi: 10.1093/ietisy/e91-d.11.2666.
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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.11.2666/_p
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@ARTICLE{e91-d_11_2666,
author={Chin-Feng TSAI, Huan-Sheng WANG, King-Chu HUNG, Shih-Chang HSIA, },
journal={IEICE TRANSACTIONS on Information},
title={Non-recursive Discrete Periodized Wavelet Transform Using Segment Accumulation Algorithm and Reversible Round-Off Approach},
year={2008},
volume={E91-D},
number={11},
pages={2666-2674},
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.},
keywords={},
doi={10.1093/ietisy/e91-d.11.2666},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Non-recursive Discrete Periodized Wavelet Transform Using Segment Accumulation Algorithm and Reversible Round-Off Approach
T2 - IEICE TRANSACTIONS on Information
SP - 2666
EP - 2674
AU - Chin-Feng TSAI
AU - Huan-Sheng WANG
AU - King-Chu HUNG
AU - Shih-Chang HSIA
PY - 2008
DO - 10.1093/ietisy/e91-d.11.2666
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2008
AB - 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.
ER -
English
