Learning OpenCV, 2nd Edition

operators. In the special case of ksize=1, the Laplacian is computed by convolution with the following

single kernel:

0

1

0

1

-4

1

0

1

0

Figure 5-16. The single kernel used by cv::Laplacian() when ksize=1

Figure 5-17: Laplace transform (upper right) of the racecar image: zooming in on the tire (circled in white)

and considering only the x-dimension, we show a (qualitative) representation of the brightness as well as

the first and second derivatives (lower three cells); the 0s in the second derivative correspond to edges, and

the 0 corresponding to a large first derivative is a strong edge

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, a point or small blob that is surrounded by lower

values will tend to maximize the negative of this function.

With this in mind, the Laplace operator can also be used as a kind of edge detector. To see how this is done,

consider the first derivative of a function, which will (of course) be large wherever the function is changing

rapidly. Equally important, it will grow rapidly as we approach an ...