IGNOU MCS 230 SOLVED ASSIGNMENT

Ques 1.

What is image acquisition? Explain Optical, Analog and Digital image processing in brief.

Ques 2.

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?

Ques 3.

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.

Ques 4.

Explain how image enhancement is better in the frequency domain as compared to spatial domain.

Ques 5.

(i) Ideal Low Pass Filters (ILPF) (ii) Butterworth Low Pass Filters (BLPF) (iii) Gaussian Low Pass filters (GLPF)

Ques 6.

Explain the following Smoothing Filter(s):

Ques 7.

 Explain the following Image Sharpening Filter(s):

Ques 8.

i) Ideal High Pass Filters (ILPF) () Butterworth High Pass Filters (BLPF)   (iii) Gaussian High  Pass filters (GLPF)

Ques 9.

Explain Mean Filters, and Median Filter with the help of a suitable example for each.

Ques 10.

Transform the RGB cube by its CMY cube. Label all the vertices. Also, interpret the colours at the edges with respect to saturation.

Ques 11.

Explain optical flow, in context of motion perception in computer vision.

Ques 12.

Explain epipolar geometry with the help of a suitable diagram in stereo vision system.

Ques 13.

What is camera calibration? Explain how it helps to estimate the intrinsic and extrinsic parameters of a camera.

Ques 14.

Explain K-means clustering methods with the help of a suitable example. Also, discuss the advantages and disadvantages of k-means clustering methods.

Ques 15.

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.

Ques 16.

Explain agglomerative hierarchical clustering and its types with the help of a suitable example.

Ques 17.

Explain Bayes classifier with the help of a suitable example. Also discuss its properties.

Ques 18.

What is image acquisition? Explain Optical, Analog and Digital image processing in brief.

Ques 19.

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?

Ques 20.

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.

Ques 21.

Explain how image enhancement is better in the frequency domain as compared to spatial domain.

Ques 22.

(i) Ideal Low Pass Filters (ILPF) (ii) Butterworth Low Pass Filters (BLPF) (iii) Gaussian Low Pass filters (GLPF)

Ques 23.

Explain the following Smoothing Filter(s):

Ques 24.

 Explain the following Image Sharpening Filter(s):

Ques 25.

i) Ideal High Pass Filters (ILPF) () Butterworth High Pass Filters (BLPF)   (iii) Gaussian High  Pass filters (GLPF)

Ques 26.

Explain Mean Filters, and Median Filter with the help of a suitable example for each.

Ques 27.

Transform the RGB cube by its CMY cube. Label all the vertices. Also, interpret the colours at the edges with respect to saturation.

Ques 28.

Explain optical flow, in context of motion perception in computer vision.

Ques 29.

Explain epipolar geometry with the help of a suitable diagram in stereo vision system.

Ques 30.

What is camera calibration? Explain how it helps to estimate the intrinsic and extrinsic parameters of a camera.

Ques 31.

Explain K-means clustering methods with the help of a suitable example. Also, discuss the advantages and disadvantages of k-means clustering methods.

Ques 32.

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.

Ques 33.

Explain agglomerative hierarchical clustering and its types with the help of a suitable example.

Ques 34.

Explain Bayes classifier with the help of a suitable example. Also discuss its properties.

Ques 35.

What is image acquisition? Explain Optical, Analog and Digital image processing in brief.

Ques 36.

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?

Ques 37.

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.

Ques 38.

Explain how image enhancement is better in the frequency domain as compared to spatial domain.

Ques 39.

(i) Ideal Low Pass Filters (ILPF) (ii) Butterworth Low Pass Filters (BLPF) (iii) Gaussian Low Pass filters (GLPF)

Ques 40.

Explain the following Smoothing Filter(s):

Ques 41.

 Explain the following Image Sharpening Filter(s):

Ques 42.

i) Ideal High Pass Filters (ILPF) () Butterworth High Pass Filters (BLPF)   (iii) Gaussian High  Pass filters (GLPF)

Ques 43.

Explain Mean Filters, and Median Filter with the help of a suitable example for each.

Ques 44.

Transform the RGB cube by its CMY cube. Label all the vertices. Also, interpret the colours at the edges with respect to saturation.

Ques 45.

Explain optical flow, in context of motion perception in computer vision.

Ques 46.

Explain epipolar geometry with the help of a suitable diagram in stereo vision system.

Ques 47.

What is camera calibration? Explain how it helps to estimate the intrinsic and extrinsic parameters of a camera.

Ques 48.

Explain K-means clustering methods with the help of a suitable example. Also, discuss the advantages and disadvantages of k-means clustering methods.

Ques 49.

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.

Ques 50.

Explain agglomerative hierarchical clustering and its types with the help of a suitable example.

Ques 51.

Explain Bayes classifier with the help of a suitable example. Also discuss its properties.

Ques 52.

What is image acquisition? Explain Optical, Analog and Digital image processing in brief.

Ques 53.

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?

Ques 54.

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.

Ques 55.

Explain how image enhancement is better in the frequency domain as compared to spatial domain.

Ques 56.

(i) Ideal Low Pass Filters (ILPF) (ii) Butterworth Low Pass Filters (BLPF) (iii) Gaussian Low Pass filters (GLPF)

Ques 57.

Explain the following Smoothing Filter(s):

Ques 58.

 Explain the following Image Sharpening Filter(s):

Ques 59.

i) Ideal High Pass Filters (ILPF) () Butterworth High Pass Filters (BLPF)   (iii) Gaussian High  Pass filters (GLPF)

Ques 60.

Explain Mean Filters, and Median Filter with the help of a suitable example for each.

Ques 61.

Transform the RGB cube by its CMY cube. Label all the vertices. Also, interpret the colours at the edges with respect to saturation.

Ques 62.

Explain optical flow, in context of motion perception in computer vision.

Ques 63.

Explain epipolar geometry with the help of a suitable diagram in stereo vision system.

Ques 64.

What is camera calibration? Explain how it helps to estimate the intrinsic and extrinsic parameters of a camera.

Ques 65.

Explain K-means clustering methods with the help of a suitable example. Also, discuss the advantages and disadvantages of k-means clustering methods.

Ques 66.

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.

Ques 67.

Explain agglomerative hierarchical clustering and its types with the help of a suitable example.

Ques 68.

Explain Bayes classifier with the help of a suitable example. Also discuss its properties.

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