000185744 001__ 185744
000185744 005__ 20181203023054.0
000185744 0247_ $$2doi$$a10.1061/(Asce)Cp.1943-5487.0000178
000185744 022__ $$a0887-3801
000185744 02470 $$2ISI$$a000313292800006
000185744 037__ $$aARTICLE
000185744 245__ $$aMultiresolution Information Mining for Pavement Crack Image Analysis
000185744 260__ $$bAmerican Society of Civil Engineers$$c2012$$aReston
000185744 269__ $$a2012
000185744 300__ $$a9
000185744 336__ $$aJournal Articles
000185744 520__ $$aEmpirical mode decomposition (EMD) is a multiresolution data analysis method recently developed to cater to the inherent nonstationarity in real-world signals. A two-dimensional (2D) extension of EMD is used in this paper as a pavement distress image analytical tool. The algorithm decomposes an image into a set of narrow band components (called bidimensional intrinsic mode function, or BIMF) that uniquely reflect the variations in the image. Although some components could have good image edge characteristics, others might hold fidelity to the shape and size of objects or trends in the image. Therefore, the complete spatial and frequency characteristic of a desired image feature might also be divided into the different components, indicating that all attributes of a desired feature might not be found in a single component. An optimal solution requires image mining from the different component resolutions to accurately extract those specific attributes without compromising certain spatial and frequency characteristics. Two major contributions to pavement image analysis are achieved. First, the paper explores pavement image denoising or enhancement by combining the EMD and a weighted reconstruction technique as a tool for background standardization of images acquired under different types of illumination effects. Second, using principal component pursuit (PCP), the authors reconstruct a composite image by selecting salient information from coarse and fine resolution BIMFs useful for accurate extraction of linear patterns in a pavement distress image. Compared with conventional image reconstruction or approximation techniques, the methodology used in this paper yields better results. DOI:10.1061/(ASCE)CP.1943-5487.0000178. (C) 2012 American Society of Civil Engineers.
000185744 6531_ $$aBackground standardization
000185744 6531_ $$aEmpirical mode decomposition
000185744 6531_ $$aImage mining
000185744 6531_ $$aImage reconstruction
000185744 6531_ $$aIntrinsic mode functions principal component pursuit
000185744 6531_ $$aPavement distress images
000185744 700__ $$uUniv Delaware, Dept Civil & Environm Engn, Newark, DE 19716 USA$$aAdu-Gyamfi, Y. O.
000185744 700__ $$uUniv Delaware, Dept Civil & Environm Engn, Newark, DE 19716 USA$$aOkine, N. O. Attoh
000185744 700__ $$uUniv Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA$$aGarateguy, Gonzalo
000185744 700__ $$0245580$$g215293$$aCarrillo, Rafael
000185744 700__ $$aArce, Gonzalo R.$$uUniv Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA
000185744 773__ $$j26$$tJournal Of Computing In Civil Engineering$$k6$$q741-749
000185744 909C0 $$xU10380$$0252392$$pLTS2
000185744 909CO $$pSTI$$particle$$ooai:infoscience.tind.io:185744
000185744 917Z8 $$x120906
000185744 917Z8 $$x215293
000185744 937__ $$aEPFL-ARTICLE-185744
000185744 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000185744 980__ $$aARTICLE