Computer Vision Techniques for Automatic Structural Assessment of Underground Pipes
Citations
849 citations
Cites background from "Computer Vision Techniques for Auto..."
..., 2014), underground concrete pipe cracks (Sinha et al., 2003), and potholes in asphalt pavement (Koch and Brilakis, 2011)....
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...DOI: 10.1111/mice.12334 cracks (Ying and Salari, 2010; Cord and Chambon, 2012; Zalama et al., 2014), underground concrete pipe cracks (Sinha et al., 2003), and potholes in asphalt pavement (Koch and Brilakis, 2011)....
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Cites background or methods from "Computer Vision Techniques for Auto..."
...In automatic classification of patterns or objects in an image, the spectral and textural attributes are used as features (Sinha et al. 2003)....
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...The algorithm proposed by Sinha et al. (2003) consists of image processing, segmentation, feature extraction, pattern recognition and a proposed neuro-fuzzy network for classification....
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...…1999, Rey et al. 2002, Bosc et al. 2003), underwater inspections (Lebart et al. 2000, Edgington et al. 2003), transportation systems (Achler and Trivedi 2004) and nondestructive structural health monitoring (Dudziak et al. 1999, Abdel-Qader et al. 2003, Sinha et al. 2003, Poudel et al. 2005)....
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...…vector dimension, it is possible to map the principal features of a pattern from a higher dimensional space to a lower dimensional space by means of a mapping transformation, such as discrete cosine transformation, Fourier transformation or PCA (Karhunen–Loeve transform) (Sinha et al. 2003)....
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...A study on using computer vision techniques for automatic structural assessment of underground pipes has been carried out by Sinha et al. (2003)....
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References
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"Computer Vision Techniques for Auto..." refers background or methods in this paper
..., textural features) distinguish objects by using statistical measures based on gray-scale co-occurrence matrix (Haralick, 1973) and its variant, such as gray-scale difference vector, moment invariants, and gray-scale difference matrix....
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...Transform coefficient feature extraction has proved practical in several applications in which the transform domain features are used as inputs to a pattern recognition classification system (Haralick, 1973; Shaikh and Tian, 1996)....
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...Textural features are those characteristics such as smoothness, fineness, coarseness, or a particular pattern associated with an image (Haralick, 1973)....
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...The second categories (i.e., textural features) distinguish objects by using statistical measures based on gray-scale co-occurrence matrix (Haralick, 1973) and its variant, such as gray-scale difference vector, moment invariants, and gray-scale difference matrix....
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