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vol="26" pubyear="2012">
          <page end="749" page_count="9" begin="741">741-749</page>
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          <title type="source">JOURNAL OF COMPUTING IN CIVIL ENGINEERING</title>
          <title type="source_abbrev">J COMPUT CIVIL ENG</title>
          <title type="abbrev_iso">J. Comput. Civil. Eng.</title>
          <title type="abbrev_11">J COMP CIV</title>
          <title type="abbrev_29">J COMPUT CIVIL ENG</title>
          <title type="item">Multiresolution Information Mining for Pavement Crack Image Analysis</title>
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            <display_name>Adu-Gyamfi, Y. O.</display_name>
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                <first_name>Gonzalo</first_name>
                <last_name>Garateguy</last_name>
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                <first_name>Gonzalo R.</first_name>
                <last_name>Arce</last_name>
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              <full_address>Ecole Polytech Fed Lausanne, Inst Elect Engn, CH-1015 Lausanne, Switzerland</full_address>
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            <subheading>Technology</subheading>
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          <subjects count="6">
            <subject ascatype="traditional">Computer Science, Interdisciplinary Applications</subject>
            <subject ascatype="traditional">Engineering, Civil</subject>
            <subject ascatype="extended">Computer Science</subject>
            <subject ascatype="extended">Engineering</subject>
            <subject ascatype="traditional">COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS</subject>
            <subject ascatype="traditional">ENGINEERING, CIVIL</subject>
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        <keywords count="6">
          <keyword>Background standardization</keyword>
          <keyword>Empirical mode decomposition</keyword>
          <keyword>Image mining</keyword>
          <keyword>Image reconstruction</keyword>
          <keyword>Intrinsic mode functions principal component pursuit</keyword>
          <keyword>Pavement distress images</keyword>
        </keywords>
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              <p>Empirical 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.</p>
            </abstract_text>
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        <bib_id>26 (6): 741-749 NOV-DEC 2012</bib_id>
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