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#Gaussian kernel how to

This chapter discusses many of the nice and peculiar properties of the Gaussian kernel.

#Gaussian kernel how to

Only available on Jetson Xavier devices.įor information on how to use the performance table below, see Algorithm Performance Tables.īefore comparing measurements, consult Comparing Algorithm Elapsed Times.įor further information on how performance was benchmarked, see Performance Benchmark. The Gaussian (better Gauian) kernel is named after Carl Friedrich Gau (1777-1855), a brilliant German mathematician.

Input and output images must have same image format and size.

Kernel size must be at least 1x1 and at most 11x11.

Note We clamp the minimum kernel size to 3 because a kernel with size 1 doesn't have enough samples to properly characterize a Gaussian function.ĭestroy a stream instance and deallocate all HW resources.įor more information, see Gaussian Filter in the "API Reference" section of VPI - Vision Programming Interface.Ĭonstraints for specific backends supersede the ones specified for all backends. Gaussian filter is implemented as a convolution operation on the input image where the kernel has the following weights: This is called a negative Laplacian because the central peak is negative. It is also known as the squared exponential kernel. There are different ways to find an approximate discrete convolution kernal that approximates the effect of the Laplacian. RBF (lengthscale 1.0, lengthscalebounds (1e-05, 100000.0)) source ¶ Radial-basis function kernel (aka squared-exponential kernel).

Use both user-provided kernel support size and filter standard deviation. This two-step process is call the Laplacian of Gaussian (LoG) operation.

Kernel support size is automatically calculated based on the filter standard deviation (sigma).It produces images with less artifacts than Box Filter, but could potentially be more costly to compute. Gaussian Filter is a low-pass discrete Gaussian filter that smooths out the image by doing a Gaussian-weighted averaging of neighbor pixels of a given input pixel.