@article{Canny:1986:ACA,
number = {6},
volume = {8},
optnote = {CANNY86},
author = {F. John Canny},
optstatus = {OK, URL, Paper, RI},
localfile = {papers/Canny.1986.ACA.pdf},
title = {A {C}omputational {A}pproach to {E}dge {D}etection},
abstract = {This paper describes a computational approach to edge detection.
The success of the approach depends on the definition of a
comprehensive set of goals for the computation of edge points.
These goals must be precise enough to delimit the desired behavior
of the detector while making minimum assumptions about the form of
the solution. We define detection and localisation criteria for a
class of edges, and present mathematical forms for these criteria
as functionals on the operator impulse response. A third criteria
is then added to ensure that the detector has only one response to
a single edge. We use the criteria in numerical optimisation to
derive detectors for several common image features, including step
edges. On specialising the analysis to step edges we find that
there is a natural uncertainty principle between detection and
localisation performance, which are the two main goals. With this
principle we derive a single operator shape which is optimal at
any scale. The optimal detector has a single approximate
implementation in which edges are marked at maxima in gradient
magnitude in a Gaussian smoothed image. We extend this simple
detector using operators of several widths to cope with different
signal to noise ratios in the image. We present a general method
called feature synthesis for the fine to coarse integration of
information from operators at different scales. Finally we show
that step edge detector performance improves considerably as the
operator point spread function is extended along the edge. This
detection scheme uses several elongated operators at each point,
and the directional operator outputs are integrated with the
gradient maximum detector.},
doi = {http://doi.acm.org/10.1145/11274.11275},
journal = jIEEEPAMI,
pages = {679698},
year = {1986},
}
