Hierarchical clustering for automated line detection

An approach to line detection based on hierarchical stepwise segmentation is developed. Pixels are grouped into line support regions based on the criteria of spatial contiguity and similarity of average gradient orientation. Subpixel equations of the lines are computed from these line support region data through plane fitting and principal component analysis. Four methods of computing subpixel line equations from the detected line support regions are presented, and their performances are compared using both synthetic and real test images. The line support regions produced by the hierarchical segmentation are of good quality. The evaluation of the line estimation schemes shows that the summary statistics method provides excellent estimates of line equations in addition to the simply computed measure of line goodness