Image Edge Detection Using Edge Function

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Afzal
Afzal il 7 Feb 2020
Risposto: Rajith il 19 Nov 2023
I would like to detect edges in the image in the attached TestImage.jpg. I've highlighted my region of interest in (ROI.jpg). I would not like to use a fixed ROI matrix as different images may not always fit the mask. So I would like to do some edge detection to automatically detect my ROI. I've tried the following:
% Read image as gray scale
I = rgb2gray(imread('TestImage.jpg'));
I = im2double(I);
BW = edge(I,'Canny',0.75,sqrt(150));
image(BW,'CDataMapping','scaled')
The results (DetectedEdges.jpg) does not seem to resolve all the edges I require. I've tried different threshold values as well. Is there a better way of getting my ROI?

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Image Analyst
Image Analyst il 7 Feb 2020
First I'd get a first shot at the mask by thresholding. Luckily much of the surround is white so thresholding will get the bottom and sides. Then I might try a texture filter, like stdfilt(), because you know your object of interest is rough while the curved bars/rods behind it are smooth. Then threshold that texture mask and AND it with your intensity thresholding mask. If that doesn't work, post the code for it and we can start tweaking it.
  9 Commenti
Image Analyst
Image Analyst il 9 Feb 2020
Modificato: Image Analyst il 9 Feb 2020
Well at least if you can get a slightly larger mask, that would help. You could use that ot get rid of 90% of the clutter and then refine it from there. Definitely see if you can lock down everything as tight as you can. Consult your professional machine shop if you need help.
I don't think you would need to do segmentation in two different color spaces. Doing it in HSV color space alone should be fine. Whatever you do in RGB colorspace you could do probably easier in HSV color space.
You'll also want to calibrate your system. That means putting in an x-rite Color Checker Chart so that you can get calibrated and consistent HSV values and not have them change every time the lighting changes. And of course have controlled lighting, preferably in a concealed light booth where your scene is not subject to ambient lighting changes in the lab or factory.
By the way, I think you attached the wrong images. The image of the hat looks nothing like the image of that metal bar, or whatever it is, that you attached in your original post.
Afzal
Afzal il 6 Mar 2020
Modificato: Afzal il 6 Mar 2020
I have attempted a solution using a for loop over various thresholds and generating a polygon from closed white areas using mask2poly function from file exchange. I then compare the polygons with my fixed mask using region props (compare Area, Perimeter, MajorAxisLength) to see which region is most similar to my mask. So far it works well. But I want to further improve the mask boundary as there are dark regions in my ROI that get cropped out due to the threshold value that works well for most of the ROI (see DetectedROI.png).
I have attached a figure of the boundaries detected by mask2poly function for one of the ROIs, and overlayed a Savitzky-Golay filter. I want to patch the regions where there are steep changes in gradients (highlighted as the orange points), the desired result being shown by the drawn black lines. Do you know a way of doing this. I was thinking of calculating the average slope just before the gradient steepens and then continuing along the gradient until I meet the boundary again. But I would not want to do this at the edges, as the gradient is expected to steepen there. I have also attached the filtered boundary points (ROIBoundaryPoints.mat).

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Rajith
Rajith il 19 Nov 2023
function out = edgy(in)
% Get the size of the input image
[r, c] = size(in);
% Create an output array that is two rows and columns smaller
out = zeros(r-2,c-2);
% Use the size of the new array for looping
[r, c] = size(out);
% Convert to double for doing calculations
in = double(in);
% Create the horizontal and vertical edge detector filters
ex = [-1 0 1; -2 0 2; -1 0 1];
ey = [1 2 1; 0 0 0; -1 -2 -1];
for ii = 1:r
for jj = 1:c
sx = in(ii:ii+2,jj:jj+2) .* ex;
sy = in(ii:ii+2,jj:jj+2) .* ey;
% Calculate the output pixel value
out(ii,jj) = sqrt(sum(sum(sx(:)))^2 + sum(sum(sy(:)))^2);
end
end
% Convert back to uint8
out = uint8(out);
end

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