An improved multifractal method for pavement cracks extraction
Purpose - This paper aims to provide an improved multifractal method to extract the pavement cracks in the complicated background. Further-more, the pavement surface images with or without crack can also be distinguished by this method. Design/methodology/approach - The framework of analyzing the image singularity is based on the sub-pixel multifractal measure (SPMM). Performing the SPMM can give the sub-pixel local distribution of the image gradient and a more precise singularity exponent distribution in the image. Meantime, using the singularity exponents and the most singular manifold (MSM), the image can be decomposed into a series of sets with different statistical and physical properties automatically and easily. One can extract the cracks according to the MSM. Findings - The example shows that the physical and geometrical properties of the pavement images can be obtained by analyzing the distribution of singularity exponents and the greatest singularity exponent. The simulation results show that the SPMM has higher quality factor in the image edge detection. And the MSM detected this way reflects the most important information of the image. Originality/value - Performing the SPMM can give a more precise singularity exponent distribution in the image.
extraction paved areas flow