Digital Image Processing Using Scilab Pdf Apr 2026

// Dilation dilated = imdilate(binary, se);

// Gradient magnitude edge_magnitude = sqrt(Gx.^2 + Gy.^2); imshow(uint8(edge_magnitude)); // Prewitt prewitt_x = [-1 0 1; -1 0 1; -1 0 1]; // Laplacian (second derivative) laplacian = [0 -1 0; -1 4 -1; 0 -1 0]; edges_laplacian = imfilter(gray_img, laplacian); 7. Morphological Operations Requires binary images.

// 5. Edge detection sobel_x = [-1 0 1; -2 0 2; -1 0 1]; Gx = imfilter(double(img), sobel_x); Gy = imfilter(double(img), sobel_x'); edges = sqrt(Gx.^2 + Gy.^2);

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. digital image processing using scilab pdf

// 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

// 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.

// 3. Denoise with median filter img = medfilt2(img, [3 3]); // Dilation dilated = imdilate(binary, se); // Gradient

// Threshold to create binary image binary = gray_img > 128; // Structuring element (disk of radius 3) se = [0 1 0; 1 1 1; 0 1 0];

// Install SIVP from ATOMS (Scilab’s package manager) atomsInstall("SIVP") // Restart Scilab after installation // Load the toolbox exec("SCI/modules/sivp/macros/sivp_loader.sce", -1) Alternatively, use core functions ( imread , imshow , imwrite ) available in recent Scilab versions. 3.1 Read, Display, and Write Images // Read an image img = imread('camera.jpg'); // Display image imshow(img);

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); Edge detection sobel_x = [-1 0 1; -2

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Creative Commons Attribution 4.0 International (CC BY 4.0) Last updated: 2025

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

// Closing (dilation followed by erosion) closed = imclose(binary, se); 8.1 Simple Thresholding // Global threshold threshold = 120; segmented = gray_img > threshold; imshow(segmented); 8.2 Otsu’s Thresholding // Compute Otsu threshold automatically [level, intensity] = otsu_thresh(gray_img); bw_otsu = gray_img > level; 8.3 Connected Components Labeling [labeled_img, num_objects] = bwlabel(bw_otsu); disp("Number of objects detected: " + string(num_objects)); 9. Fourier Transform for Frequency Domain Processing // Compute FFT F = fft2(double(gray_img)); F_shifted = fftshift(F); // Magnitude spectrum magnitude = log(abs(F_shifted) + 1); imshow(magnitude, []);

// Compute histogram hist = imhist(gray_img); plot(hist); // Apply histogram equalization eq_img = histeq(gray_img); imshow(eq_img); min_val = min(gray_img); max_val = max(gray_img); stretched = (gray_img - min_val) / (max_val - min_val) * 255; 4.3 Gamma Correction gamma = 0.5; // darkens midtones corrected = 255 * (double(gray_img)/255)^gamma; 5. Filtering and Noise Reduction 5.1 Adding Noise noisy_img = imnoise(gray_img, 'gaussian', 0, 0.01); noisy_img = imnoise(gray_img, 'salt & pepper', 0.05); 5.2 Mean Filter (Low-pass) // 3x3 averaging kernel h = (1/9) * ones(3,3); filtered = imfilter(gray_img, h); 5.3 Median Filter (Non-linear) Better for salt-and-pepper noise: