opencv,image-processing,image-recognition,edge-detection,noise-reduction

(This is not a Python answer, since I never used the Python/OpenCV binding. The images below were created using Mathematica. But I just used basic image processing functions, so you should be able to implement that in Python on your own.) A very general "trick" in image processing is to...

matlab,image-processing,edge-detection

Your mask is being divided by the wrong coefficients. You normalize each coefficient by sum(abs(b(:))) or sum(abs(c(:))) to ensure that when you filter using convolution masks, the output dynamic range matches the input. In your case, you need to divide by 6 and not 256. That's why you have a...

image-processing,edge-detection,distortion,sobel

I assume that you have lines of 1 pixel width in your image that are brighter or darker than their surroundings and you want to find them and remove them from the image and replace the removed pixels by an average of the local neighborhood. I developed an algorithm for...

image,matlab,image-processing,gradient,edge-detection

From your comments, you said you want to find the gradient of the overall image. I'm assuming you mean to compute one magnitude value for the entire image. One way to do it is perhaps to find the average magnitude value. That can be done by simply finding the overall...

c#,opencv,image-processing,edge-detection

OpenCV has solution for you, but it depends on accuracy. You can start from approximate chain -- it will approximate your shape by polygonal curve and gives you vertex coordinates. You can always make it more precise testing it bypointpolygontest OpenCV function or fitting by fitLine function. Sure you need...

The problem is that the 'image()' command doesn't scale your image, so the difference between 0 and 1 is very small and not properly displayed. If you use the 'imagesc()' command instead, your image will be automatically scaled and the edges will be visible. If you then want it in...

image-processing,computer-vision,convolution,edge-detection

Based on the information given in the paper Vehicle Detection Method using Haar-like Feature on Real Time System I can't tell how the group has done it exactly. However I can suggest a way on how this could be implemented. The main difference between a haar-like feature and a convolution...

python,imagemagick,image-manipulation,edge-detection

You can do that with ImageMagick. There are different IM methods one can come up with. Here is the first algorithm which came to mind for me. It assumes the "sticky notes" are not tilted or rotated on the larger image: First stage: use canny edge detection to reveal the...

matlab,image-processing,edge-detection

assuming that by the edge points you mean white pixels, on black backround, you can do something along these lines: % some random block, simulating your case >> a_blk = rand(10,5)-0.5; % edge pixels are greater than 0 (as a assume). >> sum(sum(a_blk>0)) ...

Inside the inner for loop, filter will be a cell with only one entry containing the current filter name. Still it is a cell array, so the edge function returns an error. You will can access the content of filter, i.e. the string with the current filter name, by using...

matlab,image-processing,computer-vision,depth,edge-detection

An intuitive definition of an edge in a depth image is where the surface normal faces away from the viewer. Assuming a viewing direction [0 0 -1] (into the XY plane) any normal that has nearly vanishing z component can be characterized as an edge. e = abs( depth(:,:,3) )...

image,image-processing,imagemagick,edge-detection,canny-operator

I am not sure I understand your question, but think I can maybe get you close to an answer! I would maybe generate your green and red files separately, but let's start with what you have got. If you convert your red and lime difference file to a black and...

matlab,image-processing,edge-detection,mathematical-morphology,connected-components

You are assuming CC is an array but it's actually a structure. Specifically, this is what the docs say about bwconncomp: bwconncomp Find connected components in binary image. CC = bwconncomp(BW) returns the connected components CC found in BW. BW is a binary image that can have any dimension. CC...

matlab,image-processing,image-segmentation,edge-detection

Use a Gaussian filter to clean up the noise before applying the edge detector: % Create the gaussian filter with hsize = [5 5] and sigma = 3.5 G = fspecial('gaussian',[7 7], 3.5); Note1f = imfilter(Note1,G,'same'); Edge1f=edge(Note1f,'sobel'); sub_Note1f = imcrop(Edge1f,rect_Note1); figure(6), imshow(sub_Note1f); This results in a much cleaner 100 image...

ruby-on-rails,heroku,imagemagick,edge-detection

Here is a potential option for using at least ImageMagick 6.8 on Heroku with the Cedar-14 stack: https://github.com/ello/heroku-buildpack-imagemagick-cedar-14

python,scipy,edge-detection,sobel

This is a difficult image to apply simple edge detection due to the stone and concrete textures. The texture makes it almost as though you have a very noisy image to which you are applying first derivative. You'll end up with many small undesired edges. Here is your code working...

You can try yourself by increasing the threshold. Here You are finding biggest contour on thresholded image, so display thr just after threshold() using imshow() and see what going on , and how it's look like. See the result by increasing the threshold to little higher value. threshold(thr, thr,100,...

matlab,image-processing,edge-detection

Consider the following code: I = im2double(rgb2gray(..)); [rows,cols] = size(I); N = zeros(size(I)); for i=2:rows-1; for j=2:cols-1; N(i,j) = 1*I(i-1,j-1) + 1*I(i-1,j) + 1*I(i-1,j+1) + ... 0 + 0 + 0 + ... -1*I(i+1,j-1) + -1*I(i+1,j) + -1*I(i+1,j+1); end end O = zeros(size(I)); for i=2:rows-1; for j=2:cols-1; O(i,j) = 1*I(i-1,j-1)...

matlab,image-processing,filter,dicom,edge-detection

Your images are just too big. The Laplacian operator is a 3x3 matrix that approximates the second derivative of the images. Your image is 2700x2200 which means that the variability of the pixels in that small size(comparing to the whole image) is neglectable. You can see it better if you...

java,image-processing,edge-detection,sobel

In your for loops when you check if (i==0 || i==width-1 || j==0 || j==height-1) you should probably be checking for i >= width-2 rather than i==width-1. For example, if width is 10 it falls into the statement if i == 9. you want to catch if i == 8...

Since you can specify whether you want horizontal or vertical edge detected (check here), you could perform 2 filtering operations (one horizontal and the other vertical) and save each resulting image, then concatenating them to form a final, 3-channels RGB image. The RGB color code for yellow is [1 1...

python,image-processing,numpy,fft,edge-detection

If you find the 2D FFT method unsatisfactory, you might consider attacking this problem using opencv since the toolkit is highly developed and provides many tools suitable for the problem you describe. One potential strategy: construct an image pyramid from the image in question. Then, perform an edge detection operation...

matlab,computer-vision,grayscale,edge-detection

The correct answer is the first one : dark = 75 and light = 230, relative to the range of values in each image graythresh uses the min and max values in the image as boundaries, which is the most logical behavior....