Hi Muhammad,
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:
% Read the input image
inputImage = imread('pothole_image.jpg');
% 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:
- Read the input image and convert it to grayscale.
- Normalize the image intensity values to the range [0, 1].
- Reshape the normalized image into a 1D array for FCM input.
- Set the number of clusters you want to identify (e.g., 2 for pothole vs. background).
- Set the FCM options, including the fuzziness exponent, maximum number of iterations, and convergence threshold.
- Run FCM clustering on the image using the fcm function, which returns the cluster centers and membership values.
- Find the maximum membership value for each pixel to determine the cluster assignment.
- Reshape the cluster assignment array back to the original image size.
- Display the segmented image using imshow and set the colormap for visualization.
- Label the detected potholes in the segmented image using the bwlabel function.
- Overlay the detected potholes on the original image by assigning the pothole regions to a specific color.
- 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.
