# fuzzy c means clustering in potholes detection

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Muhammad Zulkifli on 15 May 2023
Commented: Walter Roberson on 20 May 2023
hi all
this is my image. I would like to detect potholes using fuzzy c mean algorithm . Can you share the code and explain step to do it

Shaik on 15 May 2023
Certainly! Fuzzy C-means (FCM) is a clustering algorithm that can be used for image segmentation, including pothole detection. Here's an example code implementation in MATLAB using the Fuzzy Logic Toolbox:
% Convert the image to grayscale
grayImage = rgb2gray(inputImage);
% Normalize the image
normalizedImage = double(grayImage) / 255;
% Reshape the image into a 1D array
imageVector = reshape(normalizedImage, [], 1);
% Set the number of clusters
numClusters = 2;
% Set the FCM options
options = [2; 100; 1e-5; 0];
% Run FCM clustering
[center, U] = fcm(imageVector, numClusters, options);
% Find the maximum membership value for each pixel
[~, maxIndex] = max(U);
% Reshape the maxIndex array back to the original image size
segmentedImage = reshape(maxIndex, size(normalizedImage));
% Display the segmented image
figure;
imshow(segmentedImage, []);
% Set the colormap for visualization
colormap(gray(numClusters));
% Label the detected potholes
potholeLabel = 1; % Label for potholes
potholeImage = segmentedImage == potholeLabel;
labeledImage = bwlabel(potholeImage);
% Overlay the detected potholes on the original image
overlayImage = inputImage;
overlayImage(repmat(potholeImage, [1, 1, 3])) = 255;
% Display the overlay image
figure;
imshow(overlayImage);
title('Detected Potholes');
Please note that you will need to have the Fuzzy Logic Toolbox installed in your MATLAB environment to run this code.
Here's a step-by-step explanation of the code:
1. Read the input image and convert it to grayscale.
2. Normalize the image intensity values to the range [0, 1].
3. Reshape the normalized image into a 1D array for FCM input.
4. Set the number of clusters you want to identify (e.g., 2 for pothole vs. background).
5. Set the FCM options, including the fuzziness exponent, maximum number of iterations, and convergence threshold.
6. Run FCM clustering on the image using the fcm function, which returns the cluster centers and membership values.
7. Find the maximum membership value for each pixel to determine the cluster assignment.
8. Reshape the cluster assignment array back to the original image size.
9. Display the segmented image using imshow and set the colormap for visualization.
10. Label the detected potholes in the segmented image using the bwlabel function.
11. Overlay the detected potholes on the original image by assigning the pothole regions to a specific color.
12. Display the overlay image to visualize the detected potholes.
Remember to replace 'pothole_image.jpg' with the path and filename of your actual pothole image.
Muhammad Zulkifli on 20 May 2023
Thank you for the explaination.But,how can i add bounding box to it. and it can specificaly detect the pothole
Walter Roberson on 20 May 2023
No, several people discussed in your previous post why k-means will not work for you, and the same reasons apply for fuzzy c-means.