laplacianofgaussian | 易学教程

Laplacian of Gaussian

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问题 I am having trouble implementing a LoG kernel. I am trying to implement 9x9 kernal with theta = 1.4 as shown in this link http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm. However, I am having difficulty with the formula itself. If someone could tell me how to calculate the center ie what x and y values to use in order to get -40 in the 9x9 kernel it'd be greatly appreciated. 回答1: You don't need to worry about the formula - that is just used to generate the coefficients. You just need to apply

Laplacian of gaussian filter use

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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

Python implementation of the laplacian of gaussian edge detection

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问题 I am looking for the equivalent implementation of the laplacian of gaussian edge detection. In matlab we use the following function [BW,threshold] = edge(I,'log',...) In python there exist a function for calculating the laplacian of gaussian. It is not giving the edges back definitely. scipy.ndimage.filters.gaussian_laplace Any pointer to online implementation or the code Thanks 回答1: What matlab edge() do should be Compute LoG Compute zero crossings on LoG Compute a threshold for local LoG

A faster approach to Laplacian of Gaussian

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I am currently in the process of optimizing my code to make image processing more efficient. my first problem was due to the vision.VideoFileReader and step where it took a long time to open each frame. I speed up my code by compressing my grayscale image into 3 frames in 1 RGB frame. This way I could load 1 RGB frame using vid.step() and have 3 frames imported ready for processing. Now my code is running slow on the Laplacian of Gaussian (LoG) filtering. I read that using the function imfilter can be used to perform a LoG but it appears to be the next rate limiting step. Upon further reading,

Python implementation of the laplacian of gaussian edge detection

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I am looking for the equivalent implementation of the laplacian of gaussian edge detection. In matlab we use the following function [BW,threshold] = edge(I,'log',...) In python there exist a function for calculating the laplacian of gaussian. It is not giving the edges back definitely. scipy.ndimage.filters.gaussian_laplace Any pointer to online implementation or the code Thanks ycyeh What matlab edge() do should be Compute LoG Compute zero crossings on LoG Compute a threshold for local LoG difference Edge pixels = zero crossing && local difference > threshold The LoG filter of scipy only does

Laplacian of gaussian filter use

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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

Is Laplacian of Gaussian for blob detection or for edge detection?

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问题 The following code is provided from (was asked to remove the link). But I was wondering how it exactly works. I was confused if this was considered edge detection or blob detection, as Wikipedia list the Laplacian of Gaussian (LoG) as blob detection. Also, could somebody explain and provide a deeper explanation for why the absolute value is calculated and what is going on in the focus_stack() function? # Compute the gradient map of the image def doLap(image): # YOU SHOULD TUNE THESE VALUES TO