  Edges of an image are considered a crucial type of information. These can be
extracted by applying edge detectors with different methodology. Edge detection
is a vital step in computer vision tasks, because it is an essential issue for
pattern recognition and visual interpretation. In this paper, we propose a new
method for edge detection in images, based on the estimation by kernel of the
probability density function. In our algorithm, pixels in the image with
minimum value of density function are labeled as edges. The boundary between
two homogeneous regions is defined in two domains: the spatial/lattice domain
and the range/color domain. Extensive experimental evaluations proved that our
edge detection method is significantly a competitive algorithm.
