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Iteration-Free Bi-Dimensional Empirical Mode Decomposition and Its Application
Taravichet TITIJAROONROJ Kuntpong WORARATPANYA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2017/09/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
iteration-free computation, locally partial correlation for principal component analysis (LPC-PCA), bi-dimensional empirical mode decomposition (BEMD), bi-intrinsic mode functions (BIMF), iteration-free bi-dimensional empirical mode decomposition (iBEMD), principal component analysis (PCA),
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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.