1(a)
With a neat block diagram, explain the components of a general purpose image processing system.

10 M

1(b)
Draw a neat cross sectional view of human eye and label its parts.

6 M

1(c)
Discuss brightness discrimination and plot the typical weber ratio curves.

4 M

2(a)
With neat diagrams, explain image acquisition using linear and circular sensor strips.

10 M

2(b)
Let the set of gray levels used to define connectivity be {94, 95, 96,97} and compute the shortest D

(p) 96 97 94 97

98 98 100 96

99 97 98 95

(q) 97 96 97 96 Fig.Q2(b)

_{4}and D_{8}distance between pixels p and q for the image segment shown in Fig.Q2(b). Indicate the shortest path by double lines.(p) 96 97 94 97

98 98 100 96

99 97 98 95

(q) 97 96 97 96 Fig.Q2(b)

4 M

2(c)
Let p and q are the two pixel at coordinates (100, 120) and (130, 160) respectively. Compute:

i) Eucliden distance

ii) Chess board distance,

iii) Mahattan distance.

i) Eucliden distance

ii) Chess board distance,

iii) Mahattan distance.

6 M

3(a)
Give any three properties of unitary transforms.

6 M

3(b)
Compute the 2D-DFT of the 4×4 gray scale image give bye \[u\left ( m ,n \right )=\begin{bmatrix}1 & 1 & 1 & 1\\
1& 1& 1 & 1\\
1 & 1& 1& 1\\
1& 1& 1& 1
\end{bmatrix}\]

4 M

3(c)
For the 2×2 transform A and the image U,\[A=\frac{1}{2}\begin{bmatrix}
\sqrt{3} &1 \\
-1 & \sqrt{3}
\end{bmatrix},U=\begin{bmatrix}
2 & 1\\
1 & 2
\end{bmatrix}\] Calculate the transformed image V and the basis images.Also reconstruct the original image U by inverse transform.

10 M

4(a)
Genrate Haar basis for N=2.

8 M

4(b)
Compute the K-L transform of the following matrix:\[X = \begin{bmatrix}
4 &-2 \\
-1& 3
\end{bmatrix}
\]

12 M

5(a)
Discuss histogram equalization for contrast enhancement.

10 M

5(b)
For the image shown in Fig.Q5(b), plot the histograms before and after the histogram equalization.\[\begin{bmatrix}
4 & 3& 5& 3& 4\\
4& 4& 4& 4& 4\\
5& 3& 5& 3& 5\\
4& 4& 4& 4& 4\\
4& 3& 5& 3& 4
\end{bmatrix}\] Fig.Q5(b)

10 M

6(a)
Filter the image shown in Fig.Q6(a) by using a 3×3 median filter mask and hence prove that median filtering minimizes salt and pepper noise.\[\begin{bmatrix}
24 & 23& 33& 25& 32& 24\\
34& 255& 24& 0& 26& 23\\
23& 21& 32& 32& 28& 26
\end{bmatrix}\] Fig.Q6(a)

10 M

6(b)
Explain a filtering approach for simulatneous dynamic range compression and contrast enhancement

10 M

7(a)
Discuss adaptive median filtering method for image restoration. Also give its advantages.

10 M

7(b)
Derive the expression for observed image when the degradation are linear, position invariant.

10 M

8(a)
Explain the procedure for converting colors from RGB to HIS and vice-versa.

10 M

8(b)
Explain the concept of intensity slicing for psuedocolor image processing.

10 M

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