Base Convolution with a kernel.
Kernel can be IM_INT or IM_FLOAT, but always IM_GRAY. Use kernel size odd for better results.
Supports all data types. The border is mirrored.
Returns zero if the counter aborted. Most of the convolutions use this function.
If the kernel image attribute "Description" exists it is used by the counter.
Repeats the convolution a number of times.
Returns zero if the counter aborted.
If the kernel image attribute "Description" exists it is used by the counter.
Convolve with a kernel rotating it 8 times and getting the absolute maximum value.
Kernel must be square.
The rotation is implemented only for kernel sizes 3x3, 5x5 and 7x7.
Supports all data types except IM_COMPLEX. Returns zero if the counter aborted.
If the kernel image attribute "Description" exists it is used by the counter.
Difference(Gaussian1, Gaussian2) using gaussian repetitions.
Supports all data types, but if source is IM_BYTE or IM_USHORT destiny image must be of type IM_INT.
Convolution with a laplacian of a gaussian kernel.
Supports all data types, but if source is IM_BYTE or IM_USHORT destiny image must be of type IM_INT.
Finds the zero crossings of IM_INT and IM_FLOAT images. Crossings are marked with non zero values indicating the intensity of the edge. It is usually used after a second derivative, laplace.
Extracted from XITE, Copyright 1991, Blab, UiO http://www.ifi.uio.no/~blab/Software/Xite/
First part of the Canny edge detector. Includes the gaussian filtering and the nonmax suppression.
After using this you could apply a Hysteresis Threshold, see imProcessHysteresisThreshold.
Image must be IM_BYTE/IM_GRAY.
Implementation from the book:
J. R. Parker
"Algoritms for Image Processing and Computer Vision"
WILEY
int imGaussianStdDev2Repetitions
(
float
stddev
)
Calculates the number of 3x3 gaussian repetitions given the standard deviation.
int imGaussianStdDev2KernelSize
(
float
stddev
)
Calculates the kernel size given the standard deviation.