Image Processing Exam Questions And Solutions Apr 2026

Final mapping: 0→4, 2→6, 4→8, 6→11, 10→13, 14→15 Q7. Explain the steps to perform edge detection using the Sobel operator. Include masks and a brief example.

Extract 3×3 neighborhood around row3,col3 (value=10) – rows 2-4, cols 2-4 (1-indexed):

10 10 20 10 10 20 10 10 20 Gx convolution at center: (-1×10)+(0×10)+(+1×20) + (-2×10)+(0×10)+(+2×20) + (-1×10)+(0×10)+(+1×20) = (-10+0+20) + (-20+0+40) + (-10+0+20) = 10 + 20 + 10 = 40. Gy = 0 (uniform vertically). Magnitude = 40 → strong vertical edge. Q8. Convolution and correlation are identical operations in image processing. Solution: False. In convolution, the kernel is flipped (rotated 180°) before applying; correlation does not flip. Image Processing Exam Questions And Solutions

Sobel operator approximates gradient using two 3×3 masks:

Output pixel = Q6. Perform histogram equalization on a 4-bit image (0-15) with histogram: Gray level: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Frequency: 2 0 1 0 1 0 2 0 0 0 1 0 0 0 1 0 Total pixels = 8 Final mapping: 0→4, 2→6, 4→8, 6→11, 10→13, 14→15

10 12 14 12 10 14 14 14 10 Flatten: [10,12,14,12,10,14,14,14,10] Sorted: [10,10,10,12,12,14,14,14,14] Median (5th value) =

| Spatial Domain | Frequency Domain | |----------------|------------------| | Operates directly on pixels | Operates on Fourier transform of image | | Uses masks/kernels (e.g., Sobel, averaging) | Uses filters (low-pass, high-pass) | | Faster for small kernels | Faster for large kernels (using FFT) | | Intuitive for local operations | Better for periodic noise removal | Q5. Given a 5×5 image region (pixel values): not a point operation.

10 12 12 14 16 12 10 12 14 16 12 12 10 14 16 14 14 14 10 18 16 16 16 18 20 Compute the output of a at center position (row 3, col 3) – 1-indexed (value=10). Use zero-padding.

| r_k | freq | CDF | CDF_norm = CDF/8 | Equalized = round(15 × CDF_norm) | |-----|------|-----|------------------|----------------------------------| | 0 | 2 | 2 | 0.250 | 4 | | 1 | 0 | 2 | 0.250 | 4 | | 2 | 1 | 3 | 0.375 | 6 | | 3 | 0 | 3 | 0.375 | 6 | | 4 | 1 | 4 | 0.500 | 8 | | 5 | 0 | 4 | 0.500 | 8 | | 6 | 2 | 6 | 0.750 | 11 | | 7 | 0 | 6 | 0.750 | 11 | | 8-14| 0 | 6 | 0.750 | 11 | | 10 | 1 | 7 | 0.875 | 13 | | 14 | 1 | 8 | 1.000 | 15 |

c) Median filtering – it is a spatial operation using a neighborhood, not a point operation. Q2. In a 3×3 median filter applied to a grayscale image, the output pixel value is: a) Mean of the 9 neighbors b) Middle value after sorting the 9 neighbors c) Most frequent value d) Weighted sum of neighbors