HELP CANNY David Young
Nov 1992, revised Jan 1995
LIB *CANNY implements an edge detector based on some of the ideas of
Canny (Canny, J. F. 'A computational approach to edge detection', IEEE
PAMI 8(6), 679-698, November 1986). The main thing omitted is the
integration of multiple scales.
There is a short example in TEACH *CANNY.
CONTENTS - (Use g to access required sections)
-- Algorithm
-- ... Gradient estimation
-- ... Non-maximum suppression
-- ... Hysteresis thresholding
-- The canny procedure
-- Gradients as features
-- Size of output arrays
-- Other procedures
-- Garbage
-- Algorithm ----------------------------------------------------------
The algorithm is in three parts.
-- ... Gradient estimation --------------------------------------------
The first derivatives of the image in the X and Y directions are
estimated at each pixel.
In effect, the image is smoothed with a circular Gaussian mask, and then
differentiated along X and Y. In actuality, the image is convolved with
a 1-D Gaussian mask oriented along Y, and the result of this convolved
with 1-D first-derivative-of-Gaussian mask oriented along X, to get the
X derivative estimates. Similarly for Y derivatives.
The square root of the sum of the squares of the X and Y derivatives is
found for each pixel, to estimate the gradient magnitude (called just
the gradient in what follows).
-- ... Non-maximum suppression ----------------------------------------
Each pixel in the gradient image is set to zero unless it is a local
maximum along a line oriented along the gradient direction. The pixels
that are left therefore lie on the peaks of ridges in the gradient.
The gradient direction at each pixel is estimated from the X and Y
derivatives (i.e. tan(angle from X-axis to direction) = (Y derivative) /
(X derivative), though we never calculate this explicitly). Estimates of
neighbouring gradients are then made as follows:
A / B
* * P *
/
/
/
/
/
* O *
/
/
/
/
C / D
* Q * *
/
/<- direction of grey-level gradient
/
O is at the centre of the pixel we're interested in, and each * is at
the centre of one of its nearest 8 neighbours. We identify the pixels A,
B, C and D which are closest to gradient line (so there are 4 cases),
and get the gradient at each of these four pixels. The gradient at P is
then estimated by linear interpolation between A and B and that at Q by
linear interpolation between C and D. O is a maximal pixel if the
gradient there is greater than the estimates for P and Q.
-- ... Hysteresis thresholding ----------------------------------------
The gradient array (now rather sparse) is further reduced by
thresholding. Hysteresis is used so that lines that include strong and
weak gradients are not split up. This requires two thresholds; call them
T1 and T2, with T1 < T2.
Roughly, edges start from pixels with gradients of T2 or more; they then
extend over any connected pixels with gradients of T1 or more. If you
think of following an edge, you need a gradient of T2 to start but you
don't stop till you hit a gradient below T1.
In other words, for a given pixel, if the gradient magnitude is below T1
it is unconditionally set to zero. If the gradient is at least T2 the
pixel is left alone. If the gradient is between these two, then it is
set to zero unless there is a path from this pixel to a pixel with a
gradient above T2; the path must be entirely through pixels with
gradients of at least T1. The path can include diagonal moves - i.e. we
use 8-connectivity.
This algorithm does not use the gradient direction estimates to follow
edges - it simply treats an edge as a connected set of pixels (see
*APPBLOBS). This seems to make very little difference to the final
results.
-- The canny procedure ------------------------------------------------
canny(IMAGE, SIGMA, T1, T2, XDIFF, YDIFF, GRADIENTS)
-> (XDIFF, YDIFF, GRADIENTS)
IMAGE is an array containing the data. The procedure is fastest if
this is a packed array of single precision floats as created by
*NEWSFLOATARRAY. The bounds of the array do not have to start at 1,
or at any other specific value.
SIGMA is a positive real number giving the SIGMA parameter (see
*GAUSSMASK) for smoothing in the first stage.
T1 and T2 are the thresholds for the third stage, and they should
both be positive real numbers, unless T1 is , in which case
no thresholding is carried out and T2 is ignored.
XDIFF and YDIFF are arrays, preferably created using
*NEWSFLOATARRAY, which are filled with the separate X and Y
derivative estimates, in case they are wanted. XDIFF, YDIFF or both
may be given as , in which cases one or two new arrays are
created and returned. If they are arrays, XDIFF and YDIFF have to
be the correct size to receive the gradients. The easiest way to get
this right is to give these arguments on the first call to canny,
and then re-use the arrays returned for subsequent calls with the
same size image.
GRADIENTS is the main result. It is the array of gradient estimates,
after decimation by non-maximum suppression and hysteresis
thresholding. Many of its elements will therefore be zero. Again,
this may be an array to fill or to make a new array.
The XDIFF, YDIFF and GRADIENTS arguments may be omitted. This is
equivalent to giving them all as .
-- Gradients as features ----------------------------------------------
The output array will contain many values of zero and some values
greater than or equal to T1, which are gradient estimates. If you
actually want a binary array in which edges are marked by a single value
(say 1.0), then you can use -float_threshold- (see
*FLOAT_ARRAYPROCS/float_threshold), like this:
float_threshold(0, T1/2, 1, GRADIENTS, GRADIENTS) -> GRADIENTS
Using T1/2 as the threshold guards against rounding errors. This form
will overwrite the GRADIENTS array; give the last argument as if
you want to create a new array.
-- Size of output arrays ----------------------------------------------
The output arrays will be smaller than the input array by an amount
depending on SIGMA, because you cannot apply the convolution operators
right up to the boundaries of an array. However, GRADIENTS(X, Y) will
always correspond to IMAGE(X, Y) - i.e. you do not have to worry about
this reduction in size when you look at positions in the arrays.
If you really want GRADIENTS to be as big as the original array, then
you must copy the data into a larger array with a margin big enough for
the convolution masks to reach into when processing pixels near the
boundaries. Use -diffgaussmask_limit(SIGMA)- from *GAUSSMASK to find out
how big the margin should be and -region_expand- from *BOUNDSLIST_UTILS
to get the boundslist for the new array. You then have to put something
sensible in the margin: zeroes, or a reflection of the data in the array
might make sense, and *ARRAYSAMPLE or *ARRAY_REFLECT can be used to
create the large array and do the copying and reflections very simply.
However, it is probably much better to accept that gradients near the
boundaries of the data cannot be properly defined, and to work with the
smaller output array. If your programs are properly written to use the
boundslists of the arrays they process, this should present no problem.
For example, you can pass GRADIENTS into *STRAIGHT_HOUGH and the lines
found will be correctly positioned in the coordinates of the original
image.
-- Other procedures ---------------------------------------------------
The separate stages are implemented by -canny1-, -canny2- and -canny3-,
which can be called separately if necessary. See LIB *CANNY for their
arguments and results.
--- $____________________popvision/help/canny
--- Copyright University of Sussex 1995. All rights reserved. ----------