Probe Data Normalization for Street Path Identification

The patent transforms the fixed threshold parameter into a dynamic gradient-based parameter. Instead of using a constant threshold value, the system computes gradients at each pixel location and determines threshold values relative to local gradient magnitudes. This allows the segmentation to adapt to local variations in traffic density, accurately separating streets from noise around freeways while maintaining automation through algorithmic gradient computation.

The patent implements self-service by enabling the segmentation algorithm to automatically adjust its own threshold parameters based on the input data characteristics. The gradient computation and local maximum detection mechanisms allow the system to autonomously identify appropriate threshold values for each region without human intervention. The algorithm serves itself by using the data's own gradient structure to determine segmentation criteria, eliminating the need for manual threshold tuning while maintaining high accuracy.

The patent applies local quality by making the amplification factor spatially variable rather than uniform. The amplification is determined by local gradient characteristics and proximity to detected local maxima, creating different amplification zones across the map. Areas near identified street patterns receive higher amplification to enhance detection, while regions dominated by freeway noise receive different treatment. This localized approach allows small streets to be detected while preventing uniform amplification of noise artifacts.