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  • Research Article
  • Open Access

Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition

EURASIP Journal on Advances in Signal Processing20082008:861701

  • Received: 5 September 2007
  • Accepted: 2 March 2008
  • Published:


Crack evaluation is essential for effective classification of pavement cracks. Digital images of pavement cracks have been analyzed using techniques such as fuzzy set theory and neural networks. Bidimensional empirical mode decomposition (BEMD), a new image analysis method recently developed, can potentially be used for pavement crack evaluation. BEMD is an extension of the empirical mode decomposition (EMD), which can decompose nonlinear and nonstationary signals into basis functions called intrinsic mode functions (IMFs). IMFs are monocomponent functions that have well-defined instantaneous frequencies. EMD is a sifting process that is nonparametric and data driven; it does not depend on an a priori basis set. It is able to remove noise from signals without complicated convolution processes. BEMD decomposes an image into two-dimensional IMFs. The present paper explores pavement crack detection using BEMD together with the Sobel edge detector. A number of images are filtered with BEMD to remove noise, and the residual image analyzed with the Sobel edge detector for crack detection. The results are compared with results from the Canny edge detector, which uses a Gaussian filter for image smoothing before performing edge detection. The objective is to qualitatively explore how well BEMD is able to smooth an image for more effective edge detection with the Sobel method.


  • Edge Detector
  • Empirical Mode Decomposition
  • Instantaneous Frequency
  • Intrinsic Mode Function
  • Canny Edge

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Authors’ Affiliations

Department of Civil and Environmental Engineering, University of Delaware, Newark, DE 19716-3120, USA


© A. Ayenu-Prah and N. Attoh-Okine. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.