Real-time image thresholding based on sample space reduction and interpolation approach

Real-time thresholding is very essential for real-time processing. In this paper, we use pavement crack detection as an example to explain the principle of the proposed approach. Conventional visual and manual analysis approaches to pavement crack detection are very costly, time-consuming, labor-intensive, and subjective. Real-time automated detection of pavement cracks will be very useful for pavement management. We employ the proposed sample space reduction and interpolation approach for thresholding pavement images. The main idea of the proposed approach is based on the fact that the threshold values of gray-level pavement images are strongly related with the values of the mean and standard deviation of the pixel intensities. The experimental results have demonstrated that the proposed approach can determine the threshold values accurately, reliably, robustly, quickly, and automatically. It can be applied to other real-time processing tasks as well.