Gaussian normalisation of morphological size distributions for increasing sensitivity to texture variations and its applications to pavement distress classification

This paper investigates the use of morphological size distributions to characterize textures and explore their sensitivity to the presence of inhomogeneities and multiple textures. When the image texture is a result of material properties such variations in texture typically correspond to defects on the surface being imaged. An application where the analysis of texture variations is of great significance is the assessment of distresses on pavement surfaces. We develop a novel normalization scheme based on a Gaussian model to enhance the sensitivity of the morphological distributions to texture variations indicative of defects. Results from a simple rule-based classification scheme are presented to demonstrate that measures derived from the normalized distributions are useful in classifying distresses. Our use of normalized distributions, and not the original images, to develop measures for analyzing textures results in a significant reduction in computational and storage requirements.<
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