Help?
PLEASE MATCH YOUR ASSIGNMENT QUESTIONS ACCORDING TO YOUR SESSION
IGNOU MCS-230 (July 2025 – January 2026) Assignment Questions
Q1: Explain the Optical, Analog and Digital Image processing.
Q2: (i) What should be the physical size of a 2D image of a document with dimensions is 3200×2400, when scanned at 400dpi.Here dpi stands for dots per inch.
(ii) If the physical size of a medical image is 4×4 inches and the sampling resolution is 5 cycles/mm, then how many pixels per cycle are required to have a better-quality image? Will an image of size512×512 be enough?
Q3: (i) What is the storage requirement for a 2048 x 2048, 24-bitcolour image?
(ii) Explain Intensity, Contrast, Brightness, Noise and Resolution with respect to Images.
Q4: (i) Check whether the matrix 
is unary or not?
(ii) Perform the transformation g1(v) = 3v on the image 
Q5: Apply given 3×3 mask w on the following image F (x, y) to generate the new image.
Q6: What is a histogram? Find histogram of image given below:
Q7: Why is DCT important for image compression? Explain with the help of an example.
Q8: What do you understand by Image enhancement? Explain the techniques of image enhancement with a suitable example. Also discuss the advantages of image enhancement.
Q9: Explain the following Smoothing Filter(s):
(i) Ideal Low Pass Filters (ILPF) (ii) Butterworth Low Pass Filters (BLPF)
(iii) Gaussian Low Pass filters (GLPF)
Q10: What do you understand by feature extraction? What are its applications? Also discuss few traditional methods of feature extraction.
Q11: Explain image degradation and its types.
Q12: Transform the RGB cube by its CMY cube. Label all the vertices. Also, interpret the colours at the edges with respect to saturation.
Q13: Do you mean by Camera Calibration? Explain how intrinsic and extrinsic parameters of a camera are estimated?
Q14: Explain Bayesian Classification with the help of a suitable example.
Q15: Explain K-means clustering methods with the help of a suitable example. Also, discuss the advantages and disadvantages of k -means clustering methods.
Q16: 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.
IGNOU MCS-230 (July 2024 – January 2025) Assignment Questions
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 5 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):
(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.
Q10: Explain optical flow, in context of motion perception in computer vision. (5 Marks)
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.
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.
