Extraction of straight lines with subpixel precision: comparative study among three methods

Abstract

In the fields of Machine Vision and Photogrammetry, extracted straight lines
from digital images can be used either as vector elements of a digital
representation or as control entities that allow the determination of the camera
interior and exterior orientation parameters. Applications related with image
orientation require feature extraction with subpixel precision, to guarantee the
reliability of the estimated parameters. This paper presents three approaches
for straight line extraction with subpixel precision. The first approach
considers the subpixel refinement based on the weighted average of subpixel
positions calculated on the direction perpendicular to the segmented straight
line. In the second approach, a parabolic function is adjusted to the grey level
profile of neighboring pixels in a perpendicular direction to the segmented
line, followed by an interpolation of this model to estimate subpixel
coordinates of the line center. In the third approach, the subpixel refinement is
performed with a parabolic surface adjustment to the grey level values of
neighboring pixels around the segmented line. The intersection of this surface
with a normal plane to the line direction generates a parabolic equation that
allows estimating the subpixel coordinates of the point in the straight line,
assuming that this is the critical point of this function. Three experiments with
real images were made and the approach based on parabolic surface
adjustment has presented better results.