Learning OpenCV

Laplace

The OpenCV Laplacian function (first used in vision by Marr [Marr82]) implements a discrete approximation to the Laplacian operator: ^[66]

Because the Laplacian operator can be defined in terms of second derivatives, you might well suppose that the discrete implementation works something like the second-order Sobel derivative. Indeed it does, and in fact the OpenCV implementation of the Laplacian operator uses the Sobel operators directly in its computation.

void cvLaplace(
  const CvArr*  src,
  CvArr*        dst,
  int           apertureSize = 3
);

The cvLaplace() function takes the usual source and destination images as arguments as well as an aperture size. The source can be either an 8-bit (unsigned) image or a 32-bit (floating-point) image. The destination must be a 16-bit (signed) image or a 32-bit (floating-point) image. This aperture is precisely the same as the aperture appearing in the Sobel derivatives and, in effect, gives the size of the region over which the pixels are sampled in the computation of the second derivatives.

The Laplace operator can be used in a variety of contexts. A common application is to detect "blobs." Recall that the form of the Laplacian operator is a sum of second derivatives along the x-axis and y-axis. This means that a single point or any small blob (smaller than the aperture) that is surrounded by higher values will tend to maximize this function. Conversely, ...