MATLAB - Image Edge Detection using Prewitt Operator from Scratch

MATLAB – Image Edge Detection using Prewitt Operator from Scratch

Last Updated : 17 May, 2020

Prewitt Operator: It is a gradient-based operator. It is one of the best ways to detect the orientation and magnitude of an image. It computes the gradient approximation of image intensity function for image edge detection. At the pixels of an image, the Prewitt operator produces either the normal to a vector or the corresponding gradient vector. It uses two 3 x 3 kernels or masks which are convolved with the input image to calculate approximations of the derivatives – one for horizontal changes, and one for vertical –

$M_{x}=\left[\begin{array}{ccc}-1 & 0 & 1 \\ -1 & 0 & 1 \\ -1 & 0 & 1\end{array}\right] \quad M_{y}=\left[\begin{array}{ccc}-1 & -1 & -1 \\ 0 & 0 & 0 \\ 1 & 1 & 1\end{array}\right]$

Approach:

Step 1: Input – Read an image
Step 2: Convert the true-color RGB image to the grayscale image
Step 3: Convert the image to double
Step 4: Pre-allocate the filtered_image matrix with zeros
Step 5: Define Prewitt Operator Mask
Step 6: Edge Detection Process (Compute Gradient approximation and magnitude of vector)
Step 7: Display the filtered image
Step 8: Thresholding on the filtered image
Step 9: Display the edge-detected image

Implementation in MATLAB:

% MATLAB Code | Prewitt Operator from Scratch 
  
% Read Input Image 
input_image = imread('[name of input image file].[file format]'); 
  
% Displaying Input Image 
input_image = uint8(input_image); 
figure, imshow(input_image); title('Input Image'); 
  
% Convert the truecolor RGB image to the grayscale image 
input_image = rgb2gray(input_image); 
  
% Convert the image to double 
input_image = double(input_image); 
  
% Pre-allocate the filtered_image matrix with zeros 
filtered_image = zeros(size(input_image)); 
  
% Prewitt Operator Mask 
Mx = [-1 0 1; -1 0 1; -1 0 1]; 
My = [-1 -1 -1; 0 0 0; 1 1 1]; 
  
% Edge Detection Process 
% When i = 1 and j = 1, then filtered_image pixel   
% position will be filtered_image(2, 2) 
% The mask is of 3x3, so we need to traverse  
% to filtered_image(size(input_image, 1) - 2 
%, size(input_image, 2) - 2) 
% Thus we are not considering the borders. 
for i = 1:size(input_image, 1) - 2 
    for j = 1:size(input_image, 2) - 2 
  
        % Gradient approximations 
        Gx = sum(sum(Mx.*input_image(i:i+2, j:j+2))); 
        Gy = sum(sum(My.*input_image(i:i+2, j:j+2))); 
                 
        % Calculate magnitude of vector 
        filtered_image(i+1, j+1) = sqrt(Gx.^2 + Gy.^2); 
         
    end
end
  
% Displaying Filtered Image 
filtered_image = uint8(filtered_image); 
figure, imshow(filtered_image); title('Filtered Image'); 
  
% Define a threshold value 
thresholdValue = 100; % varies between [0 255] 
output_image = max(filtered_image, thresholdValue); 
output_image(output_image == round(thresholdValue)) = 0; 
  
% Displaying Output Image 
output_image = im2bw(output_image); 
figure, imshow(output_image); title('Edge Detected Image'); 

Input Image –

Filtered Image:

Edge Detected Image:

Advantages:

Good performance on detecting vertical and horizontal edges
Best operator to detect the orientation of an image

Limitations:

The magnitude of coefficient is fixed and cannot be changed
Diagonal direction points are not preserved always

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