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based on an earlier version of the Wolfram Language.
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CrossingDetect

CrossingDetect[image]
gives a binary image in which white pixels correspond to the zero crossings in image.
CrossingDetect
treats values in image that are smaller in absolute value than delta as zero.
  • CrossingDetect finds pixels with positive values that have at least one negative neighbor.
  • CrossingDetect is typically used in edge-detection algorithms based on second derivatives.
  • CrossingDetect will only find zero crossings when applied to images of type .
  • For color images, CrossingDetect operates on the intensity averaged over all channels.
  • CrossingDetect[m] finds zero crossings in a numerical matrix m, returning a sparse array.
  • CrossingDetect effectively chops values smaller in magnitude than delta.
  • CrossingDetect by default treats all eight pixels surrounding a given pixel as adjacent.
  • The option setting CornerNeighbors->False treats only the four pixels in the coordinate directions as adjacent.
  • Using the option setting CornerNeighbors->None, CrossingDetect operates on the dual grid whose pixels correspond to the corners in the original image, thereby reducing the dimensions of the resulting image by one pixel.
Detect edges by finding zero crossings in a second derivative image:
Find a contour line in an elevation raster:
Find zero crossings in a matrix:
Detect edges by finding zero crossings in a second derivative image:
In[1]:=
Click for copyable input
Out[1]=
 
Find a contour line in an elevation raster:
In[1]:=
Click for copyable input
Out[1]=
 
Find zero crossings in a matrix:
In[1]:=
Click for copyable input
Out[1]//MatrixForm=
Compute the crossing detect using 4- or 8-connectivity:
This operates on the dual grid:
Improved edge detection using the product of zero crossings and the first derivative of an image:
The zero crossings of a LoG filtered image are closed contours:
New in 8