laplacian

Laplacian kernels of higher order in image processing

由 痴心易碎 提交于 2020-01-30 07:55:06
问题 In literature on digital image processing you find examples of Laplace kernels of relatively low orders, typically, 3 or 5. I wonder, is there any general way to build Laplace kernels or arbitrary order? Links or/and references would be appreciated. 回答1: The Laplace operator is defined as the sum of the second derivatives along each of the axes of the image. (That is, it is the trace of the Hessian matrix): ∇ I = ( ∂ 2 /∂ x 2 + ∂ 2 /∂ y 2 ) I There are two common ways to discretize this: Use

Laplacian kernels of higher order in image processing

由 杀马特。学长 韩版系。学妹 提交于 2020-01-30 07:54:39
问题 In literature on digital image processing you find examples of Laplace kernels of relatively low orders, typically, 3 or 5. I wonder, is there any general way to build Laplace kernels or arbitrary order? Links or/and references would be appreciated. 回答1: The Laplace operator is defined as the sum of the second derivatives along each of the axes of the image. (That is, it is the trace of the Hessian matrix): ∇ I = ( ∂ 2 /∂ x 2 + ∂ 2 /∂ y 2 ) I There are two common ways to discretize this: Use

Laplacian of gaussian filter use

由 别说谁变了你拦得住时间么 提交于 2019-12-21 03:44:32
问题 This is a formula for LoG filtering: (source: ed.ac.uk) Also in applications with LoG filtering I see that function is called with only one parameter: sigma(σ). I want to try LoG filtering using that formula (previous attempt was by gaussian filter and then laplacian filter with some filter-window size ) But looking at that formula I can't understand how the size of filter is connected with this formula, does it mean that the filter size is fixed? Can you explain how to use it? 回答1: As you've

Laplacian of gaussian filter use

由 六眼飞鱼酱① 提交于 2019-12-03 11:06:35
This is a formula for LoG filtering: (source: ed.ac.uk ) Also in applications with LoG filtering I see that function is called with only one parameter: sigma(σ). I want to try LoG filtering using that formula (previous attempt was by gaussian filter and then laplacian filter with some filter-window size ) But looking at that formula I can't understand how the size of filter is connected with this formula, does it mean that the filter size is fixed? Can you explain how to use it? As you've probably figured out by now from the other answers and links, LoG filter detects edges and lines in the
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