Laplacian Edge Detection

In Poor Man's ADC Revisit, I demonstrated gradient based edge detection using Python's Imaging Library to read in a jpg. You can also use the second derivative (a 2D Laplacian operator) to detect edges. Very similar to the gradient based scheme, just change the finite differences to give an approximation to the second derivative rather than the first.

# calculate second derivative for all three channels, sum of squares for mag
for y in xrange(2,h-1):
for x in xrange(2,w-1):
rdx = inpix[x+1,y][0] + inpix[x-1,y][0] - 2*inpix[x,y][0]
gdx = inpix[x+1,y][1] + inpix[x-1,y][1] - 2*inpix[x,y][1]
bdx = inpix[x+1,y][2] + inpix[x-1,y][2] - 2*inpix[x,y][2]
rdy = inpix[x,y+1][0] + inpix[x,y-1][0] - 2*inpix[x,y][0]
gdy = inpix[x,y+1][1] + inpix[x,y-1][1] - 2*inpix[x,y][1]
bdy = inpix[x,y+1][2] + inpix[x,y-1][2] - 2*inpix[x,y][2]
outpix2[x,y] = math.sqrt( rdx*rdx + gdx*gdx + bdx*bdx + rdy*rdy + gdy*gdy + bdy*bdy )

To perform image segmentation it would probably be smart to use the pixel value, the gradient (mag and direction) and the second derivative, no sense in throwing away useful information. Gray-scale image of the magnitude of the second derivative (edges are really where the second derivative changes sign, but an edge tends to have extrema in the second derivative on either side of it) shown below.


Using Psyco or F2Py has been shown on similar problems to give several orders of magnitude speed up over a straight Python implementation as shown above.