MCS-230 Solved Assignment 2024-25

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MCSL-230 Solved Assignment 2024-25 Available

Q1: What is image acquisition? Explain Optical, Analog and Digital image processing in brief.
Q2: If the physical size of a medical image is 4 × 4 inches and the sampling resolution is
cycles/mm, then how many pixels per cycle are required to have a better-quality image?Will an image of size 512 × 512 be enough?
Q3: Explain the types of Images based on (i) Attributes (ii) Based on Colour
Q4: Solve the following problems:
a. What is the storage requirement for a 2024 x 2024, 24-bit colour image?
b. Calculate pixel resolution of a camera in mega pixels, capturing an image of dimension: 3000 X 4000
c. Given an image is a gray scale image with aspect ratio of 8:2 and pixel resolution of
1000000 pixels, calculate the dimensions and the size of the image.
Q5: Explain how image enhancement is better in the frequency domain as compared to spatial domain.
Q6: Explain the following Smoothing Filter(s):
(i) Ideal Low Pass Filters (ILPF) (ii) Butterworth Low Pass Filters (BLPF) (iii) Gaussian Low Pass filters (GLPF)
Q7: Explain the following Image Sharpening Filter(s): (5 Marks)
(i) Ideal High Pass Filters (ILPF) (ii) Butterworth High Pass Filters (BLPF) (iii) Gaussian High Pass filters (GLPF)
Q8: Explain Mean Filters, and Median Filter with the help of a suitable example for each.
Q9: Transform the RGB cube by its CMY cube. Label all the vertices. Also, interpret the colours at
the edges with respect to saturation. (5 Marks)
Q10: Explain optical flow, in context of motion perception in computer vision.
Q11: Explain epipolar geometry with the help of a suitable diagram in stereo vision system.
Q12: What is camera calibration? Explain how it helps to estimate the intrinsic and extrinsic parameters of a camera.

Q13: Explain K-means clustering methods with the help of a suitable example. Also, discuss the
advantages and disadvantages of k -means clustering methods.
Q14: Perform partitional clustering using Frogy’s method for the data given in the table below with k-2 (two clusters). Use first two sample points (3,3) and (6,8) as seed points.
S. No. X Y
1 3 3
2 6 8
3 10 10
4 4 4
5 6 6
6 14 12
7 20 18
8 22 20
Q15: Explain agglomerative hierarchical clustering and its types with the help of a suitable example.
Q16: Explain Bayes classifier with the help of a suitable example. Also discuss its properties.

Q13: Explain K-means clustering methods with the help of a suitable example. Also, discuss the
advantages and disadvantages of k -means clustering methods. (5 Marks)
Q14: Perform partitional clustering using Frogy’s method for the data given in the table below with k-2
(two clusters). Use first two sample points (3,3) and (6,8) as seed points. (5 Marks)
S. No. X Y
1 3 3
2 6 8
3 10 10
4 4 4
5 6 6
6 14 12
7 20 18
8 22 20
Q15: Explain agglomerative hierarchical clustering and its types with the help of a suitable example.
Q16: Explain Bayes classifier with the help of a suitable example. Also discuss its properties