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Journal of Electronic Imaging

Fast segmentation of industrial quality pavement images using Laws texture energy measures and k-means clustering
Author(s): Senthan Mathavan; Akash Kumar; Khurram Kamal; Michael Nieminen; Hitesh Shah; Mujib Rahman
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Paper 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.

Paper Details

Date Published: 16 September 2016
PDF: 11 pages
J. Electron. Imag. 25(5) 053010 doi: 10.1117/1.JEI.25.5.053010
Published in: Journal of Electronic Imaging Volume 25, Issue 5
Show Author Affiliations
Senthan Mathavan, Nottingham Trent Univ. (United Kingdom)
Akash Kumar, National Univ. of Sciences and Technology (Pakistan)
Khurram Kamal, National Univ. of Sciences and Technology (Pakistan)
Michael Nieminen, Fugro Roadware (Canada)
Hitesh Shah, Fugro Roadware (Canada)
Mujib Rahman, Brunel Univ. (United Kingdom)


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