Unit-I - DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS

Part- A

1. For a pixel at (m,n), who are its neighbor.2. Specify the order of operation O(n) for DFT and FFT.3. How an image can be digitized?4. Bring out the difference between the discrete Fourier transform and Fast Fourier

transform.5. What are the properties of DFT.6. Define non-uniform sampling.7. For a square image with N=128 with 256 gray levels, find the storage

requirement.8. What is the advantage of separablity property?9. What is an optimum transform?10. What is the application of separable property of a image transform?

Part- B1. a) Show that the principal component transform is optimal in mean square error

sense. b) State and explain the properties of Discrete Fourier transform and obtain DFT

of .

2. Draw a block diagram of an image processing system and explain its functional parts.

3. Explain two dimensional sampling theory in detail. 4. What are practical limitations in sampling and reconstruction? 5. What are the properties of Discrete Cosine Transform? Why is it commonly used

in image processing? 6. Discuss the fundamental steps in Image processing7. a) What are the types of connectivity used in establishing boundaries of objects?

b) Define 2D discrete cosine transform and compute DCT for the following

A=

8. a) Bring out the Kernels and their matrix representation for Walsh and Hadamard where N=8.b) How is image model developed and interpreted?

9. a) For a shift invariant system, show that the output becomes convolution of input and impulse response.b) Check whether the following matrix is unitary and orthogonal.

A=

c) Explain the over all monochrome vision model.

10. a) Discuss about optimum mean square quantizer.b)What are the practical limitations of sampling and reconstruction?

11. a) Define Discrete cosine transform and list its properties.b) For the given orthogonal transform matrix

A=

Find the transformed image and basis images, if image

u =

12. a) Explain about the teeplitz and circulant matrices.b) With examples, explain the unitary and orthogonal matrices.

13. a) Explain the two dimensional sampling theory in detail.b) What are practical limitations in sampling and reconstruction?

14. a) List the properties of 2D discrete Fouries transform.b) Explain Hadamard transform and find H16

15. Prove the convolution theorem. 16. Show each of the following

i) A circular matrix is Toeplitz, but the converse is not true. ii) The product of two circular matrices is a circular matrix. iii) The product of two Toeplitz matrices need not be Toeplitz. Show that a band limited image cannot be space limited and vice versa.

17. a) If the KL transform of a Zero mean NX1 vector U is Ǿ , then show that the KL Transform of the sequence. u(n)= u(n) + µ 0 ≤ n ≤ (N-1) Remains the same only if the vector 1 (1,1,…….1) T

Is an eigen vector of the co-variance matrix of u. b) State the properties of SVD transform. c) Prove that an NXN Haar transform Matrix is orthogonal and can be implemented in o(n) Operation on an NX1 vector.

18. t

Unit II - IMAGE ENHANCEMENT TECHNIQUES:

Part A1. Draw the histogram of dark image and low contrast images.2. Draw the filter function of the 2D ideal high pass filter.3. What is meant by image preprocessing?4. What is meant by frequency Histogram of an image?5. Define Image smoothing.6. What is meant by Image sharpening?7.

Part B1. a) What is unsharp masking? Perform Unsharp masking on

b) How derivative filters are used to sharpen image. 2. Compare image smoothing and image sharpening. List out their merits and demerits. Give example. 2. Describe reduction by image averaging.3. Explain Image sharpening with the Laplacian operator.

a) Explain edge detection based on derivative operators.b) How do you apply Hough transform to link edges in an image?

4. a) If f(x,y) and η (x,y) denote original image and noise respectively, what is the effect of image averaging?

b) What is Histogram equalization? Equalize the following 4 bit image matrix

5. a) What are t he masks used for spatial domain filtering?Explain.b) When is CMY colour model preferred? Why?

6. a) Explain contrast stretching and histogram techniques for image enhancement. b) Explain transform based enhancement and colour image enhancement.

7. Describe the following operation.a) Image Negative b) Histogram Equalizationc) Median filtering

8. a) Define a spatially invariant system .b) The image f(x,y)=4 cos4лx .cos 4лy is sampled with Δx=Δy=0.5 and

Δx=Δy=0.2.The reconstruction filter is an ideal low pass filter with BW[(1/2) Δx,(1/2) Δy] what is the reconstructed image in each case?

c) Define the compandor transformations for zero mean Gaussian Random Variables.

9. a) Explain two Dimensional orthogonal and Unitary transforms?b) Define the properties of Hadamard transform?

10. a) Explain histogram equalization for image Enhancement.b)Explain the techniques of unsharp masking and crispening.c) State Root filetering.

11. a) Describe median filtering ratio?b)Explain Aliasing and fold over frequencies.

12. Explain the Histogram modeling technique for image enhancement13. Explain the Spatial Averaging and Median filtering Technique. 14. State the properties of the A Sine transform. 15. Explain the Principal Component Analysis. 16. Convolve the following

x (m,.n) =

h (m,n) =

17. Explain detail about 2D Sampling and Quantization. 18. Show that the real orthogonal matrix is unitary but a unitary matrix need not be

orthogonal.19. How colour vision is represented and the associated model is developed20. What are the properties of 2 dimensional unitary transform. 21. Explain the Usage and properties of method of principal components. 22. Explain 2D sampling and reconstruction using samples. 23. Explain the principle and features of Lloyd - Max quantizer.24. What are the Basis images and how they are used to represent the image?25. Explain in discrete convolution in 2D.

i) Specify the conditions for a matrix to be orthogonal and unitary. ii) If ‘A’ is a matrix, when does it become Block Toeplitz?

26. Given a band limited real world-image, explain the process of sampling and reconstruction.

27. What is a compandor? What is the use of compandor in quantization?28. Define 2D Discrete Fourier transform and compute DFT for the following:

29. Write the Hadamard transform and list the properties of Hadamard transform.30. Explain the Advantages of Cepstrum and Homomorphic filtering.31. Explain the following with respect to enhancement. i) Clipping ii) Thresholding

iii) Bit extraction. 32. Explain Spatial and transform filtering. 33. Describe generalized cepstrum and Homomorphic filtering. 34. How would you perform the following? i)Bit extraction ii)Generalized cepstrum

and Homomorphic transformation35. fweew

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5 3 2 4

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4 7

5 6

Unit III - IMAGE RESTORATION

Part A1. Draw the pdf of Erlang Noise.2. Why geometric transformation is called as Rubber sheet transformation.3. What do you mean by preprocessing?4. Draw the perspective plot and radial cross section of ideal high pass filter. Specify

its transfer function.5. What do you mean by intensity transformation?6. What is the need for padding?7. Give a method of restoring a corrupted image in the absence of noise.8. Explain the digital image implementation of the Wiener filter.

Part B1. a) Model the exponential and impulse noise. b) How additive periodic noise is removed by restoration in frequency domain. 2. a) How to order statistic filters get applied to remove noise? b) Explain how images are restored using constrained least squares filtering.3. a)What are the difficulties faced in image acquisition. b)What is image restoration? Explain. 4. Describe the situation where in Kalman filtering is applied in image

processing applications. 5. a)Discuss the effect of the Role of illumination on global thresholding.

b)Explain a split and merge iterative algorithm with an example 6. a) Explain edge detection based on derivative operators.

b) How do you apply Hough transform to link edges in an image?7. a) Discuss about the image observation model. b) Explain about Kalman filtering used in image restoration.8. a) Describe the situation where in Kalman filtering is applied in image processing application.

b) Develop a degradation model and suggest the solutions for it.9. In what conditions the median filters very effective? 10.Compare the Mean Square Errors of a geometric Mean filter and a Wiener

filter. 11.Explain Kalman filter. 12.Discuss about Fourier - Wiener filter.13.comment on least square filters. 14.Describe pseudo inverse restoration of image subjected to space invariant blur. 15.Discuss the effect if noise added to the blur on the restoration. 16.What is the need for constrained restoration? Explain.17.Explain the process of restoring an image from the degraded variation using

Wiener filter.

Unit IV - IMAGE COMPRESSION

Part A1. How do you find magnitude and direction angle of an image.2. If 256 X 256 8 bit image occupies 20K bytes after compression, what is the

compression ratio and redundancy?3. Distinguish Image encoding and decoding.4. What is the property of image transform that is used for compression?5. Give the principle of Entropy coding.

Part B1. a) Describe a general compression system model.

b) Explain constant area and bit plane coding techniques. 2. a) Discuss the results from Information theory.

b) Give the properties of Spectral density function.3. Explain predictive coding with motion compensation.4. Explain how to Interpixel redundancies using bit-plane coding. 5. Describe a reversible linear transform coding for image compression.6. a) Explain lossless predictive coding method in compression.

b) For a source with alphabets having the following probability P(a1)=0.35, P(a2)=0.25, P(a3)=0.3, P(a4)=0.05, P(a5)=0.05,Find arithmetic code for a1a2a3a3a4a5

7. a) How is Transform coding employed in JPEG compression standard?b) What is the standard used for Binary images? Explain.

8. a)Explain in detail about lossless predictive techniques used for image compression.b) Briefly explain three types of adaptive transform coding.

9. a)Explain pixel coding and entropy coding.b)Describe techniques used in compression.

10. a)Explain Huffman coding algorithmb)What are the limitations of bit plane coding?c)Explain Distortionless predictive coding.

11. Define the Techniques of Pixel coding and Entropy coding. 12. Explain the Dimensional DPCM Coding Technique13. Explain with examples. i) Entropy coding. ii) Runlength coding 14. What is the principle of Entropy coding? 15. Describe Huffman coding technique with an example. 16. Describe various orthogonal gradient edge detection techniques. 17. Discuss the performance of these techniques in noisy environments 18. Explain how delta modulation and Differential Pulse Code modulation techniques

can be used for image compression.19. Write short notes on i) Adaptive Transform Coding ii) Runlength Coding 20.

Unit V - IMAGE SEGMENTATION AND REPRESENTATION

Part A1. Develop the signature representation of an equilateral triangle.2. What is the expression of nth moment?3. How do you represent the boundaries of an image?4. What is the purpose of feature extraction?5. Define Texture.6. Give two examples of morphological operations.

7.

Part B1. a) How do you perform Laplacian edge detection?

b) How do you process Globally an image by Hough transform to link edges.2. a) How do you represent boundary by polygonal approximations.

b) For the following boundary, obtain the shape number and signature.

3. a) How dilation and erosion are used to ‘bridging gaps’ and ‘eliminating irrelevant details’ respectively in a binary image. b) How Fourier descriptor and Euler number are generated.

4. List out various image representation schemes and bring out their merits and demerits.

5. Discuss in detail about image segmentation.6. Explain the segmentation techniques used in image analysis. 7. Illustrate how to describe an image with regional descriptors.8. Enumerate rewriting rules for basic repetitive patterns in a boundary using relational

descriptors.9. a) How are 4 and 8 chain codes used to represent an object boundary?

b) What is signature? Derive the signature of a circle and a rectangle.10. a) Explain with example:’ Opening and Closing’.

b) How are Fourier descriptors computed and used?11. a) Explain the texture segmentation

b) Describe the non-supervised clustering technique.c) What is window slicing?

12. Explain about various Edge detection operators.13. a) Explain the gradient , compass edge detection operators

b) List out all the structural and statistical classification of texture.c) What are the applications of moment invariants

14. Explain the Histogram feature Extraction method. 15. Explain about shape features. 16. Explain the object recognition system based on boundary analysis.

A

A

17. Explain Texture Segmentation. 18. How would you observe Textures present in an image. 19. What are the techniques available to decompose a scene into its components?

Explain. 20. Explain the method of representing a shape using Fourier descriptors and spatial

moments. 21. Describe the generalized Dilation and Erosion processes for Binary image. Give

mathematical representation of the above processes and examples. 22. What are the statistical and structural approaches to Texture analysis?23. Describe the suitable extraction techniques use for computing geometry features such

as size and orientation.24. What is the requirement of image segmentation and explain the following image

segmentation techniques:i) Window slicing.ii) Component labeling.iii) Template matching.

OthersPart A1. Define feature vectors in pattern recognition.2. List out any two distance metrics methods in pattern recognition.3. What is the application of Ho-Kashya procedure?4. Distinguish linear classification and Non linear classification in pattern

recognition.5. List out the features of Cluster Analysis.6. What is the role of clusters in a linear classification?7. Write the features of evaluation of clustering.8. How to formulate a syntactic pattern recognition system?9. What is the role of AI method in pattern Recognition Problem?10. Write notes on Decision Theoretic method.11. Distinguish Pattern Recognition and Morphology.12. Define Non Supervised Clustering Technique.Part B1. Discuss the basic problems in pattern recognition system design. 2. Describe the perception criterion function. 3. Discuss any two partitional clustering techniques. 4. Explain the Hierarchical clustering techniques in pattern recognition. 5. With suitable example and diagram discuss the concept of pattern recognition

from formal language theory6. Write notes on

a) Applications of pattern recognition.b) Linear discriminant functions.

7. Explain spectral factorization in detail. 8. Describe in detail with neat sketches about linear prediction in two dimensions.9. What is point spread function?

10. Explain the usage of Radon transform in the process of image reconstruction from projections.

11. Define a Practical approach to colour image enhancement algorithm 12. Discuss image crispening techniques. 13. How is the colour represented in an image? 14. Explain chromaticity diagram for the spectral primary system15. Define block circulant matrix.16. Explain Delta modulation. 17. Explain the need of image Understanding.18. Compare the performance of Zero Crossing operators and Gradient operators.