Solve any one question from Q.1(a,b)& Q2.(a,b)

1(a)
Explain the Human Visual system in detail.

4 M

Solve any one question from Q.1(a,b) & Q.2(a,b)

1(a)
Explain the Human Visual system in detail.

4 M

1(b)
Explain in brief & with example three disatance measures between pixels.

3 M

1(b)
Explain in brief & with example three distance measures between pixels.

3 M

2(a)
What is image subtraction? How the pixels are scales between 0 to 255 after image subtraction. Give application of image subtraction operation.

4 M

2(a)
What is image subtraction? How the pixels are scaled between 0 to 255 after image subtraction. Give application of image substraction operation.

4 M

2(b)
Explain HSI color model of an image.

3 M

2(b)
Explain HSI color model of an image

3 M

Solve any one question from Q.3(a,b)& Q.4(a,b)

3(a)
Filter the following image using 3×3 neighbourhood averaging by assuming zero padding. \( \begin{bmatrix}
1& 2 & 3& 2\\
4 & 2& 5 & 1\\
1 & 2 & 6 & 3\\
2 & 4 & 6 & 7
\end{bmatrix} \)/

4 M

Solve any one question from Q.3(a,b) & Q.4(a,b)

3(a)
Filter the following image using 3×3 neighbourhood averaging by assuming zero padding. \[\begin{bmatrix}
1 &2 &3 &2 \\
4 & 2 & 5 & 1\\
1 &2 & 6 & 3\\
2 &4 & 6 &7
\end{bmatrix}\]

4 M

3(b)
Explain any three noise models in short.

5 M

3(b)
Explain any three noise models in short.

5 M

4(a)
Explain following operations of image enhancement.

i) Power law transformation.

ii) Contrast streching.

i) Power law transformation.

ii) Contrast streching.

4 M

4(a)
Explain following operations of image enhancement.

i) Power law transformation.

ii) Contrast streching.

i) Power law transformation.

ii) Contrast streching.

4 M

4(b)
Explain the concept of Homomorphic filtering.

3 M

4(b)
Explain the concept of Homomorphic filtering.

3 M

Solve any one question from Q.5(a,b)& Q.6(a,b)

5(a)
Compute the entropy of the image given by \( f(x,y)=\begin{bmatrix}
0& 1& 0 &0 \\
0 & 1& 2&2 \\
0& 1 & 2 & 3\\
1 & 2 & 2 & 3
\end{bmatrix} \)/

4 M

Solve any one question from Q.5(a,b) & Q.6(a,b)

5(a)
Compute the entropy of the image given by \[f(x,y)=\begin{bmatrix}
0 & 1& 0 & 0\\
0& 1& 2 & 2\\
0 & 1 & 2& 3\\
1& 2 & 2 & 3
\end{bmatrix}\]

4 M

5(b)
Explain the concept of bit plane coding.

3 M

5(b)
Explain the concept of bit plane coding.

3 M

6(a)
Draw the block diagram of JPEG base line encoder. Explain each block in short.

4 M

6(a)
Draw the block diagram of JPEG base line encoder. Explain each block in short.

4 M

6(b)
Define Lossless & Lossy compression. Explain with example how Runlenght coding technique is used for Lossless Compression.

3 M

6(b)
Define Lossless & Lossy compression. Explain with example how Runlenght coding technique is used for Lossless Compression.

3 M

Solve any one question from Q.7(a,b)& Q.8(a,b)

7(a)
What is edge detection? Compare the performance of first order & second order derivative w.r.t. an image? Which one would you prefer for detecting edges? Why?

9 M

Solve any one question from Q.7(a,b) & Q.8(a,b)

7(a)
What is edge detection? Compare the performance of first order & second order derivative w.r.t. an image? Which one would you prefer for detecting edges? Why?

9 M

7(b)
Define image segmentation. What is Region based apporach for image segmentation Explain Region growing & Region splitting and merging technique in detail.

9 M

7(b)
Define image segmentation. What is Region based approach for image segmentation Explain Region growing & Region splitting and merging technique in detail.

9 M

8(a)
Explain the following in detail.

i) Hough transform

ii) Hit or Miss transform

i) Hough transform

ii) Hit or Miss transform

10 M

8(a)
Explain the following in detail.

i) Hough transform

ii) Hit or Miss transform

i) Hough transform

ii) Hit or Miss transform

10 M

8(b)
Explain Global, adaptive and otsu's method of thresholding.

8 M

8(b)
Explain Global, adaptive and otsu's method of thresholding.

8 M

Solve any one question from Q.9(a,b)& Q.10(a,b)

9(a)
What is the need of boundary descriptor. Explain 4-directional & 8-directional chain code with example. Hence obtain the project shape represented by 8-directional chain code (clock wise)

{0, 1, 5, 0, 6, 6, 4, 4, 4, 4, 2, 2}

{0, 1, 5, 0, 6, 6, 4, 4, 4, 4, 2, 2}

8 M

Solve any one question from Q.9(a,b) & Q.10(a,b)

9(a)
What is the need of boundary descriptor. Explain 4-directional & 8-directional chain code with example. Hence obtain the object shape represented by 8-directional chain code(clock wise) {0, 1, 5, 0, 6, 6, 4, 4, 4, 4, 2, 2}

8 M

9(b)
Explain the following Regional descriptors

i) Topological Descriptors ii) Texture descriptors

i) Topological Descriptors ii) Texture descriptors

4 M

9(b)
Explain the following Regional descriptors

i) Topological Descriptors

ii) Texture descriptors

i) Topological Descriptors

ii) Texture descriptors

8 M

10(a)
Explain in detail the concept of Fourier descriptor based boundary representation. What are its advantages.

8 M

10(a)
Explain in detail the concept of Fourier descriptor based boundary representation. What are its advantages

8 M

10(b)
Explain in detail the following

i) Staistical moments

ii) Principle component Analysis.

i) Staistical moments

ii) Principle component Analysis.

8 M

10(b)
Explain in detail the following

i) Statistical moments

ii) Principle component Analysis

i) Statistical moments

ii) Principle component Analysis

8 M

Solve any one question from Q.11(a,b)& Q.12(a,b)

11(a)
What is Pattern? Explain the representation of different pattern classes.

8 M

Solve any one question from Q.11(a,b) & Q.12(a,b)

11(a)
What is Pattern? Explain the representation of different pattern classes.

8 M

11(b)
Explain Biometric based Authentication system using image processing.

8 M

11(b)
Explain Biometric based Authentication system using image processing.

8 M

12(a)
Explain Minimum distance classifiers and correlation based classifier in detail.

8 M

12(a)
Explain Minimum distance classifiers and correlation based classifier in detail

8 M

12(b)
Explain Medical application of image processing in detail.

8 M

12(b)
Eplain Medical application of image processing in detail.

8 M

More question papers from Digital Image Processing