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Fast segmentation of industrial quality pavement images using Laws texture energy measures and k-means clustering

[+] Author Affiliations
Senthan Mathavan

Nottingham Trent University, School of Architecture, Design and the Built Environment, Burton Street, Nottingham NG1 4BU, United Kingdom

Akash Kumar, Khurram Kamal

National University of Sciences and Technology, College of Electrical and Mechanical Engineering, NUST Campus, H-12, Islamabad, Pakistan

Michael Nieminen, Hitesh Shah

Fugro Roadware, 2505 Meadowvale Boulevard, Mississauga, Ontario L5N 5S2, Canada

Mujib Rahman

Brunel University, Department of Civil Engineering, Kingston Lane, Uxbridge UB8 3PH, United Kingdom

J. Electron. Imaging. 25(5), 053010 (Sep 16, 2016). doi:10.1117/1.JEI.25.5.053010
History: Received April 14, 2016; Accepted August 25, 2016
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Abstract.  Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture nonuniformities that make their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough, and expedited health monitoring of roads. In the pavement monitoring area, well-known texture descriptors, such as gray-level co-occurrence matrices and local binary patterns, are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB® and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV-based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature.

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Citation

Senthan Mathavan ; Akash Kumar ; Khurram Kamal ; Michael Nieminen ; Hitesh Shah, et al.
"Fast segmentation of industrial quality pavement images using Laws texture energy measures and k-means clustering", J. Electron. Imaging. 25(5), 053010 (Sep 16, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.053010


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