(1)Department of Electrical and Computer Engineering
(2)Department of Computer Science
Rutgers University, Piscataway, NJ 08854, USA
Computing the weighted average of the pixel values in a window
is a basic module in many computer vision operators. The process
is reformulated in a linear vector space and the role of the
different subspaces is emphasized. Within this framework
well known artifacts of the gradient based edge detectors, such
as, large spurious responses can be explained quantitatively.
It is also shown that template matching
with a template derived from the input data is meaningful
since it provides an independent measure of confidence
in the presence of the employed edge model.
The widely used three-step edge detection procedure: gradient
estimation, nonmaxima suppression, hysteresis thresholding;
is generalized to include the information provided by the
confidence measure.
The additional amount of computation is minimal and
experiments with several standard test images show the ability
of the new procedure to detect weak edges.
