IEICE TRANSACTIONS on Information
Iteration-Free Bi-Dimensional Empirical Mode Decomposition and Its Application
Summary :
A bi-dimensional empirical mode decomposition (BEMD) is one of the powerful methods for decomposing non-linear and non-stationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-the-art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.9 pp.2183-2196
- Publication Date
- 2017/09/01
- Publicized
- 2017/06/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDP7399
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Taravichet TITIJAROONROJ
KMITL
Kuntpong WORARATPANYA
KMITL
Keyword
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Taravichet TITIJAROONROJ, Kuntpong WORARATPANYA, "Iteration-Free Bi-Dimensional Empirical Mode Decomposition and Its Application" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 9, pp. 2183-2196, September 2017, doi: 10.1587/transinf.2016EDP7399.
Abstract: A bi-dimensional empirical mode decomposition (BEMD) is one of the powerful methods for decomposing non-linear and non-stationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-the-art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7399/_p
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@ARTICLE{e100-d_9_2183,
author={Taravichet TITIJAROONROJ, Kuntpong WORARATPANYA, },
journal={IEICE TRANSACTIONS on Information},
title={Iteration-Free Bi-Dimensional Empirical Mode Decomposition and Its Application},
year={2017},
volume={E100-D},
number={9},
pages={2183-2196},
abstract={A bi-dimensional empirical mode decomposition (BEMD) is one of the powerful methods for decomposing non-linear and non-stationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-the-art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.},
keywords={},
doi={10.1587/transinf.2016EDP7399},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Iteration-Free Bi-Dimensional Empirical Mode Decomposition and Its Application
T2 - IEICE TRANSACTIONS on Information
SP - 2183
EP - 2196
AU - Taravichet TITIJAROONROJ
AU - Kuntpong WORARATPANYA
PY - 2017
DO - 10.1587/transinf.2016EDP7399
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2017
AB - A bi-dimensional empirical mode decomposition (BEMD) is one of the powerful methods for decomposing non-linear and non-stationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-the-art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.
ER -
English
