Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition
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 po...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2008-05-01
|
| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://dx.doi.org/10.1155/2008/861701 |
| _version_ | 1819052660752384000 |
|---|---|
| author | Nii Attoh-Okine Albert Ayenu-Prah |
| author_facet | Nii Attoh-Okine Albert Ayenu-Prah |
| author_sort | Nii Attoh-Okine |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T12:23:22Z |
| format | Article |
| id | doaj.art-3c0f113563b449a6b9aa07e9caedb55d |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-21T12:23:22Z |
| publishDate | 2008-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-3c0f113563b449a6b9aa07e9caedb55d2022-12-21T19:04:15ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802008-05-01200810.1155/2008/861701Evaluating Pavement Cracks with Bidimensional Empirical Mode DecompositionNii Attoh-OkineAlbert Ayenu-PrahCrack 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.http://dx.doi.org/10.1155/2008/861701 |
| spellingShingle | Nii Attoh-Okine Albert Ayenu-Prah Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition EURASIP Journal on Advances in Signal Processing |
| title | Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition |
| title_full | Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition |
| title_fullStr | Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition |
| title_full_unstemmed | Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition |
| title_short | Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition |
| title_sort | evaluating pavement cracks with bidimensional empirical mode decomposition |
| url | http://dx.doi.org/10.1155/2008/861701 |
| work_keys_str_mv | AT niiattohokine evaluatingpavementcrackswithbidimensionalempiricalmodedecomposition AT albertayenuprah evaluatingpavementcrackswithbidimensionalempiricalmodedecomposition |