Digital Image Processing Using Scilab Pdf -

// Get image dimensions (rows, cols, channels) size(img) gray_img = rgb2gray(img); imshow(gray_img); 3.3 Access and Modify Pixels // Access pixel at row 100, column 150 pixel = img(100, 150, :); // Set a region of interest to black img(50:100, 50:100, :) = 0; 4. Image Enhancement 4.1 Histogram Equalization Improves contrast by spreading intensity values.

// 4. Enhance contrast img = histeq(img);

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median_filtered = medfilt2(gray_img, [3 3]); // Create Gaussian kernel (approx) gaussian_kernel = [1 2 1; 2 4 2; 1 2 1] / 16; gaussian_filtered = imfilter(gray_img, gaussian_kernel); 6. Edge Detection 6.1 Sobel Operator // Sobel kernels sobel_x = [-1 0 1; -2 0 2; -1 0 1]; sobel_y = [-1 -2 -1; 0 0 0; 1 2 1]; Gx = imfilter(double(gray_img), sobel_x); Gy = imfilter(double(gray_img), sobel_y);

Article ID: DIP-SCILAB-01 Target Audience: Engineering students, researchers, hobbyists Software Required: Scilab 6.1+ with SIVP (Scilab Image and Video Processing) toolbox 1. Introduction Digital Image Processing (DIP) involves manipulating digital images using computer algorithms. While MATLAB is the industry standard, Scilab —a free, open-source alternative—provides powerful capabilities for DIP through its SIVP (Scilab Image and Video Processing) toolbox and core functions. // Get image dimensions (rows, cols, channels) size(img)

// 6. Threshold processed = edges > 50; imshow(processed); end

// Erosion eroded = imerode(binary, se); Enhance contrast img = histeq(img); Would you like

// Opening (erosion followed by dilation) opened = imopen(binary, se);

// Write image to disk imwrite(img, 'output.png');

// Low-pass filter in frequency domain [m, n] = size(gray_img); cx = m/2; cy = n/2; radius = 30; H = zeros(m, n); for i = 1:m for j = 1:n if sqrt((i-cx)^2 + (j-cy)^2) <= radius H(i, j) = 1; end end end