Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Evaluating Pavement Cracks with Bidimensional Empirical Mode Decompositio | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 861701 7 pages doi 2008 861701 Research Article Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition Albert Ayenu-Prah and Nii Attoh-Okine Department of Civil and Environmental Engineering University of Delaware Newark DE 19716-3120 USA Correspondence should be addressed to Nii Attoh-Okine okine@ Received 5 September 2007 Accepted 2 March 2008 Recommended by Daniel Bentil 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. Copyright 2008 A. Ayenu-Prah and N. Attoh-Okine. This is an open access article distributed under the .
