User manual

Phantom Help File372

Applied filter: None Edge Laplacian 3x3 Edge Laplacian 5x5

Edge Prewitt Horizontal or Vertical

Prewitt is a method of edge detection in computer graphics, which calculates the maximum

response of a set of convolution kernels to find the local edge orientation for each pixel.

Various kernels can be used for this operation. The whole set of 8 kernels is produced by

taking one of the kernels and rotating its coefficients circularly. Each of the resulting kernels is

sensitive to an edge orientation ranging from 0° to 315° in steps of 45°, where 0° corresponds

to a vertical edge.

Applied filter: None Edge Prewitt Horizontal Edge Prewitt Vertical

The maximum response for each pixel is the value of the corresponding pixel in the output

magnitude image. The values for the output orientation image lie between 1 and 8, depending

on which of the 8 kernels produced the maximum response.

This edge detection method is also called edge template matching, because a set of edge

templates is matched to the image, each representing an edge in a certain orientation. The

edge magnitude and orientation of a pixel are then determined by the template that matches

the local area of the pixel the best.

The Prewitt edge detector is an appropriate way to estimate the magnitude and orientation of

an edge. Although differential gradient edge detection needs a rather time-consuming

calculation to estimate the orientation from the magnitudes in the x- and y-directions, the

Prewitt edge detection obtains the orientation directly from the kernel with the maximum

response. The set of kernels is limited to 8 possible orientations. However, experience shows

that most direct orientation estimates are not much more accurate. On the other hand, the set

of kernels needs 8 convolutions for each pixel, whereas the set of the kernel in gradient

method needs only 2, one kernel being sensitive to edges in the vertical direction and one to

the horizontal direction. The result for the edge magnitude image is very similar with both

methods, provided the same convolving kernel is used.

Edge Sobel Horizontal or Vertical

The Sobel operator is an operator used in image processing, particularly within edge detection

algorithms. Technically, it is a discrete differentiation operator, computing an approximation of

the gradient of the image intensity function. At each point in the image, the result of the Sobel

operator is either the corresponding gradient vector or the norm of this vector.