 MORE IN Digital Image Processing
SPPU Electronics and Telecom Engineering (Semester 7)
Digital Image Processing
December 2016
Total marks: --
Total time: --
INSTRUCTIONS
(1) Assume appropriate data and state your reasons
(2) Marks are given to the right of every question
(3) Draw neat diagrams wherever necessary

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.
4 M
4(a) Explain following operations of image enhancement.
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
10 M
8(a) Explain the following in detail.
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}
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
4 M
9(b) Explain the following Regional 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.
8 M
10(b) Explain in detail the following
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

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