Edge tracking and fitting
of line and arc segments
As a further example, we show an approach which tracks edge data in a local neighbourhood, yet fits straight line and circular arc segments
as it proceeds. It is a three pass algorithm, which operates on thinned edge data. The algorithm is not described in detail; a description
can be found in ``Robust segmentation of edge data'' in the Proc. of the 4th IEE International Conf. on Image Processing, 1992, written by A. Etemadi.

Segmentation Pass: the usual way to segment a chain of pixels is by examination of curvature. This algorithm proceeds in a slightly
diferent manner; the chained sets of pixels are broken into subsets which are nearsymmetric about an axis passing through the midpoint of the subset, but perpendicular to the line joining the end points.

Linking Adjacent Segments: a least squares fit is performed on segment points. If adjacent segments are to be merged, then they
must have the same relative direction and radius of curvature, to within a specified tolerance. A new fit may be performed on the
linked data and the process repeated.

Classification: this is done by a simple heuristic, checking the deviation of the central point from a straight line joining the end
points. Segments are classified as straight or circular.
Figures 5 and 6, below, show an example of the algorithm applied to a test image. Here, line or arcs of less than 10 pixels are rejected.
Figure 5: The test image
Figure 6: Edge tracking and fitting
[ Feature space transformation for line detection 
Representation of twodimensional boundaries ]
Comments to: Sarah Price at ICBL.
(Last update: 22th April, 1996)