Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computer and Robot Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Condition Monitoring Using Pattern Recognition Techniques on Data from Acoustic Emissions
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
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This paper summarises the results of using machine vision approach for automating condition monitoring of wooden railway sleepers. Railway sleeper inspections are currently done manually; visual inspection being the most common approach, with some deeper examination using an axe to judge the condition. Digital images of the sleepers were acquired to compensate for the human visual capabilities. Appropriate image analysis techniques were applied to further process the images and necessary features were extracted. In this particular work, crack detection was focused and features such as, number of cracks, average length of the crack and width of the crack on each sleeper were calculated and used for further pattern recognition task. In the current work, though image analysis techniques reveal important information concerning the condition of the sleeper, it cannot be directly used for classifying the condition. Hence, a pattern recognition approach has been adopted to further classify the condition of the sleeper into classes (good or bad). A Support Vector Machine (SVM) using a Gaussian kernel has achieved good classification rate (82.35%) in the current case.