16ec6203_dip_color Coded.docx

Hindusthan College of Engineering And Technology Approved by AICTE, New Delhi, Accredited with ‘A’ Grade by NAAC (An Autonomous Institution, Affiliated to Anna University, Chennai) Coimbatore – 641 032 AUTONOMOUS VI SEMESTER UG DEGREE INTERNAL EXAMINATIONS 16EC6203 - Digital Image Processing QUESTION BANK UNIT – I DIGITAL IMAGE FUNDAMENTALS PART A Questions

Marks

Cos

1.

State the definition of a digital image.

2

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2.

What is meant by pixel?

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3.

Infer the meaning of gray level.

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4.

Illustrate dynamic range in an image.

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5.

List the steps involved in Digital Image Processing.

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6.

Specify the elements of DIP system.

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7.

Indicate the distribution of cones and rods in retina.

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8.

Compare and contrast photopic and scotopic visions.

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9.

Define weber ratio.

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10.

State the significance of Mach band effect.

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11.

Interpret the term ‘simultaneous contrast’.

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12.

What do you mean by illumination and reflectance?

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13.

Paraphrase sampling and quantization.

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14.

Define Tapered Quantization.

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15.

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17.

Find the number of bits required to store a 256 X 256 image with 32 gray levels. A digital image that takes up 240kb has the spatial resolution of 600 x 200. Calculate the bit depth of that image. Compare Spatial and Gray level resolutions.

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18.

Restate the definition of Hue and Saturation.

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19.

List the hardware oriented color models.

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20.

Summarize the various applications of color models.

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16.

PART B ( 14 Marks) Questions 21.

Describe the components of image processing system in detail.

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22.

Explain in detail the fundamental steps involved in digital image processing systems.

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26.

Illustrate the principle of operation of human eye and describe that how the image formation takes place in eye. Demonstrate how the image is digitized by sampling and quantization process. Write short notes on the following terms i) Adjacency ii) Connectivity iii) Region iv)Boundary Enumerate the basic relationships between pixels along with various distance measures.

27.

Explain RGB color model along with the justification for safe color cube.

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28.

Describe HSI color model in detail with suitable diagrams and illustrations.

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23. 24. 25.

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PART C (10 Marks) 29.

Identify and summarize the applications of image processing.

10

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30.

Write the categories of digital storage for image processing applications.

10

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31.

Explain in detail about image acquisition system.

10

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32.

Discuss the effect of changing the Sampling frequency and Quantization levels in an image.

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33.

Convert the RGB model components to HSI format. When you enter a dark theater on a bright day, it takes an appreciable interval of time before you can see well enough to find an empty seat. Which of the visual processes are at play in this situation? Discuss briefly.

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34.

UNIT – II IMAGE ENHANCEMENT PART A Questions

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Cos

1.

Specify the objective of image enhancement.

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2.

Classify the methods of image enhancement and write short note on them.

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3.

List various gray level transformation technique

2

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4.

Interpret the term ‘contrast stretching’.

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5.

Differentiate contrast and brightness.

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6.

What do you mean by Point processing?

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7.

Illustrate bit plane slicing with suitable diagram.

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8.

Recognize any one application of image subtraction and write about it.

2

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9.

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2

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11.

Define Histogram. If all the pixels in an image are shuffled, will there be any change in the histogram? Justify your answer. List the limitations of averaging filter.

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12.

Express the purpose of image averaging.

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13.

State the principle of directional smoothing.

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14.

What is the need for transform?

2

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15.

Give the relation for 1-D discrete Fourier transform pair.

2

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16.

Write the 2D Fourier transform and its inverse.

2

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10.

17.

Summarize the applications of sharpening filters.

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18.

Name the different types of derivative filters.

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19.

Specify the properties of 2D Fourier transform.

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20.

Write the steps involved in frequency domain filtering.

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Questions

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21.

Enumerate the different types of gray level transformations used for image enhancement in spatial domain.

14

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22.

Compare and Contrast the concepts of histogram equalization and histogram matching in detail.

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10

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PART B ( 14 Marks)

23.

24. 25. 26. 27. 28.

Describe histogram equalization. Obtain histogram equalization for the following image segment of size 5 X 5. Write the interference on the image segment before and after equalization. 20 20 20 18 16 15 15 16 18 15 15 15 19 15 17 16 17 19 18 16 20 18 17 20 15 Discuss the limiting effect of repeatedly applying a 3 x 3 lowpass spatial filter to a digital image. You may ignore border effects. What are image sharpening spatial filters? Justify the importance of sharpening filters in enhancing an image along with the various types of it. Illustrate the image enhancement by smoothing in the frequency domain with suitable examples and illustrations. Explain the process of image enhancement in the frequency domain using sharpening filters. Develop a frequency domain procedure for improving the appearance of an image by simultaneous gray level range compression and contrast enhancement using the illumination - reflectance model. PART C ( 10 Marks)

29. 30.

How are image subtraction and image averaging is used to enhance the image? Discuss with example. Specify the enhancement changes in the monochrome image by histogram equalization. Obtain Histogram and Histogram equalization for the (4 x 4) – 4 bit per pixel is given by

31.

32. 33.

Summarize the characteristics of the Ideal, Butterworth and Gaussian filters used for smoothing and sharpening. Show that the Fourier transform and its inverse are linear processes.

UNIT – III IMAGE RESTORATION, SEGMENTATION AND MORPHOLOGICAL PROCESSING PART A Questions

Marks

Cos

1.

What is meant by Image Restoration?

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CO3

2.

Differentiate Image Enhancement and Restoration.

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CO3

3.

List three main properties of a median filter.

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CO3

4.

When will the Wiener filter reduces to an inverse filter?

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5.

Draw the model of image restoration.

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6.

List the various types of noise models.

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7.

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9.

Summarize the advantages of wiener filter over an inverse filter. A photograph is taken out of a side window of a car moving at a constant velocity of 80 kmph. Why is it not possible to use an inverse or a wiener filter in general to restore the blurring in this image? Write the expression for Impulse noise.

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10.

Formulate the function of inverse filtering.

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11.

Define the pseudo inverse filter.

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12.

Classify the three types of discontinuity in a digital image.

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13.

Define Gradient Operator along with its magnitude and derection.

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14.

Give the properties of the second derivative around an edge.

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15.

The Laplacian of a Gaussian sometimes is called the Mexican hat function. Justify the reason with neat diagram.

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16.

List the various methods of thresholding in image segmentation.

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17.

State the condition to be met by the partitions in region based segmentation.

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18.

What is the principle of region growing based image segmentation?

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19.

Differentiate between local and global thresholding technique for image segmentation.

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20.

Specify the steps involved in splitting and merging.

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21.

Write Sobel horizontal and vertical edge detection masks.

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22.

Define Erosion and Dilation.

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23.

Compare and contrast opening and closing.

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24.

Specify any two image processing applications of morphological operations.

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Marks

COs

14

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8.

PART B ( 14 Marks)

25.

Questions Discuss in details about the Arithmetic mean filter, Geometric mean filter, Harmonic mean filter and Contraharmonic mean filter.

26.

Explain in detail the removal of periodic noise using a. Band reject Filters b. Band pass Filters c. Notch Filters d. Optimum Notch Filtering

14

CO3

27.

Justify the advantage of adaptive filters in restoring an image in spatial domain by explaining adaptive local noise reduction filter and adaptive median filter.

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Marks

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28.

29. 30. 31. 32. 33.

0 0.1 0.1 0 0.1 0.1 0.1 0.1 A blur filter h(m, n) is given by ℎ(𝑚, 𝑛) = [ ]. Find the deblur 0.05 0.1 0.1 0.05 0 0.05 0.05 0 filter using ( i ) inverse filter approach ( ii ) pseudo - inverse filter approach with 𝜀 = 0.05. Describe Edge Detection by explaining the basic formulation, gradient and Laplacian operators. Enumerate the edge linking and boundary detection by local processing and global processing via Hough transform. Illustrate global processing via graph theoretic techniques with a suitable example. Give explanation about any two segmentation techniques that are based on finding the regions directly. Write about the morphological concepts of erosion, dilation , opening and closing which are applicable for image processing. PART C ( 10 Marks)

34. 35. 36. 37. 38.

Summarize the different noise models with corresponding probability distribution function and statistical features in image processing. Elucidate the process of image restoration by minimum mean square error filter in the presence of noise as well as in the absence of noise. Apply the split and merge technique to segment the image and show the quad tree corresponding to the segmentation. Explain why watershed segmentation tends to over-segment images. Also mention one solution to overcome the problem of over segmentation. Discuss how the morphological operations are applied on image processing using examples.

UNIT – IV IMAGE COMPRESSION AND WAVELETS PART A Questions 1.

List three reasons for the need of image compression.

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CO4

2.

Classify the main types of Data compression schemes.

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CO4

3.

Define relative data redundancy and compression ratio.

2

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4.

Mention the significance of psycho visual redundancy.

2

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5.

Contrast the coding and interpixel redundancies.

2

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6.

Draw a general compression system model.

2

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7.

Compare the source encoder and channel encoder.

2

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8.

Formulate the root mean square error and peak signal to noise ratio.

2

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9.

A 256 x 256 pixel digital image has eight distinct intensity levels. What is the minimum number of bits required to code this image in a lossless manner?

2

CO4

10.

Determine whether the code { 0, 01, 11 } is uniquely decodable or not.

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11.

What do you mean by the term image file format? Mention some of the frequently used image file formats.

2

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12.

Mention the limitations of Huffman coding.

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13.

Illustrate the bit plane decomposition in an image.

2

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14.

Draw the block diagram of transform coding system.

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15.

Expand JPEG.

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16.

Summarize the basic steps involved in JPEG compression standard.

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17.

Illustrate the effect of granular noise and slope overhead.

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18.

Justify the reason for zigzag scanning in JPEG compression.

2

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19.

List out some important applications of wavelet transform

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20.

Formulate the continuous wavelet transform of a one dimensional signal x(t) .

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21.

What are the advantages of DWT over DCT with respect to image compression.

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Marks

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22.

Perform Huffman algorithm for the following intensity distribution with a 64x64 image. Obtain the coding efficiency and compare with that of uniform length code. r0=1008, r1=320, r2=456, r3=686, r4=803, r5=105, r6=417, r7=301.

14

CO4

23.

A source emits letters from an alphabet A = {a1 , a2 , a3 , a4 , a5} with probabilities P(a1) = 0.2 , P(a2) = 0.4 , P(a3) = 0.2 , P(a4) = 0.1 and P(a5) = 0.1. (1) Find the Huffman code (2) Caluculate the average length of the code.

14

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24.

Generate the tag for the sequence 1 3 2 1 for the probabilities P(1) = 0.8 ,P(2) =0.02, P(3) = 0.18 using arithmetic coding.

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PART B ( 14 Marks) Questions

25. 26. 27. 28.

29.

Calculate a tag value using arithmetic coding procedure to transmit the word "INDIA". Enumerate the compression technique to reduce the interpixel redundancies using bit plane decomposition. Explain in detail about the step by step process of JPEG image compression standard. Briefly discuss about the multi resolution analysis using scaling and wavelet functions. 0 1 1 0 1 0 0 1 Calculate the Haar transform of the image [ ] 1 0 0 1 0 1 1 0 PART C ( 10 Marks)

30.

Evaluate the need for image compression. How run length encoding approach is used for compression? Is it lossy? Justify.

31. 32. 33. 34.

Obtain the Huffman code for the word "COMMITTEE". A source emits four symbols { a, b, c, d} with the probabilities 0.4, 0.2, 0.1 and 0.3 respectively. Construct arithmetic coding to encode and decode the word "dad". Explain the block diagram of the lossy predictive coding with delta modulation technique. Summarize the basic concepts of the following terms (i) Haar transform(5) (ii) Subband coding(5)

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Marks

Cos

UNIT – V IMAGE REPRESENTATION AND RECOGNITION PART A Questions 1.

Draw the 4 directional and 8- directional chain codes.

2

CO5

2.

Mention the drawbacks of the chain codes.

2

CO5

3.

State the principle of representing boundary using polygonal approximation.

2

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4.

Draw the distance versus angle signatures of a circle and square.

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5.

Illustrate convex hull and convex deficiency.

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6.

What is thinning or skeletonizing algorithm?

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7.

Draw the Medial Axis of a square with a side of length 'a'.

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8.

Does the use of chain code compress the description information of an object contour? Justify your answer.

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9.

List the properties of Fourier descriptors.

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10.

Name few boundary descriptors.

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11.

Define length of a boundary.

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12.

Develop the steps for Shape number in image segmentation.

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13.

What do you mean by connected component of a region?

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14.

How the topology is applied in image processing?

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15.

List the approaches to describe texture of a region.

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16.

State the difference between fine and coarse textures.

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17.

Define pattern and pattern classes.

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18.

Mention the three common pattern arrangements used in practice.

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19.

Classify the approached to object recognition.

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20.

Draw the basic block diagram of a face recognition system.

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21.

You are given a set of data S={Dolphin, Pomeranian dog, Humming bird, Frog, Rattlesnake, Bat}. Develop a suitable classification strategy to classify the given set S according to their nature of existence.

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22.

Distinguish between statistical and structural approaches to pattern recognition.

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Marks

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14

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PART B ( 14 Marks) Questions

Give the 4 directional chain codes for the arbitrary shapes shown in figure

23.

Represent all the boundaries of the following shapes using 8 directional chain codes.

24.

25. 26. 27. 28.

Summarize the concepts of following methods of boundary representation i) Signatures ii) Boundary segments Write in detail note about Shape numbers and Fourier descriptors. Summarize a detailed review the concepts of topological descriptors with at least two examples. Discuss in detail about the statistical, structural and spectral techniques to describe the texture of a region.

29.

List some of the object recognition methods used in image processing for decision making methods? How that methods apply in pattern classification.

14

CO5

30.

Briefly explain about the recognition techniques based on matching by a prototype pattern vector.

14

CO5

PART C ( 10 Marks) 31.

Describe any two polygonal approximation methods for representing boundaries.

10

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32.

Enumerate the boundary representation using skeletons via thinning algorithm with an example.

10

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Find the skeletons of the following shapes 33.

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34.

Explain in detail about the various applications of object recognition.

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35.

Write short notes on (i) Fingerprint recognition (ii) Face recognition with the respective classification challenges.

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Faculty In-Charge

HOD

DEAN

ADVISOR