Digital Image Processing (15EC72) - Question Bank
Digital Image
Processing (15EC72)
1.
With
the help of neat block diagram explain the components of a general purpose image
processing system. (08 Marks) Dec2017/Jan
2018
2.
With a
neat block diagram explain the fundamental steps involved in digital image
processing. (12 Marks) Dec2017/Jan 2018
3.
Explain
the image acquisition in using single sensor. (06 Marks) Dec2017/Jan 2018
4.
Consider
the image segment as shown in fig Q2(b). Set V= {0, 1}, compute the length of
shortest 4, 8, m-path between p and q, if path does not exist between p and q.
explain why.
5.
Explain
the role of sampling and quantization. (08 Marks) Dec2017/Jan 2018
6.
With a
neat block diagram explain the fundamental steps involved in digital image
processing. (10 Marks) Dec2017/Jan 2017
7.
Explain
the principle of image acquisition in using single sensor. (06 Marks) Dec2016/Jan 2017
8.
List
four applications of image processing. (04 Marks) Dec2016/Jan 2017
9.
Explain
the concept of sampling and quantization with exmples. (08 Marks) Dec2016/Jan 2017
10.
Consider
the image segment as shown.
i) Let V= {0, 1}, compute the length of
shortest 4, 8, m-path between p and q, if path does not exist between p and q.
explain why.
ii) Repeat for V={1,2}
11.
Define
spatial and grey level resolution. (02 Marks) Dec2016/Jan 2017
12.
Explain
the fundamental steps in digital image processing. (08 Marks) June/July 2017
13.
Explain
the mass storage capability in image processing applications and also its
principal categories (07 Marks) June/July
2017
14.
Write
a note on optical illusions categories (04 Marks) June/July 2017
15.
Consider
the two image subset s1 and s2 as shown in fig Q2(a). For v={1}, determine
whether these two subsets are i) 4-adjacent ii) 8 –adjacent and iii)
m-adjacent. (06 Marks) June/July 2017
16.
Explain
the process of generating digital signal (08 Marks) June/July 2017.
17.
Explain
linear and non linear operations in digital image processing (06 Marks) June/July 2017
18.
With
the help of neat block diagram explain the components of a general purpose
image processing system. (10 Marks) June/July2016
19.
Draw a
neat cross sectional view of human eye and label its parts. (06 Marks) June/July2016
20.
Discuss
brightness discrimination and plot the typical weber ratio curves. (04 Marks) June/July2016
21.
With
neat diagrams explain image acquisition using linear and circular sensor
stripes(10 Marks) June/July2016
22.
Let
the set of grey levels used to define the connectivity be {94,95,96,97} and
compute the shortest D4 and D8 distances between p and q for the image segment
shown in fig Q2(b). Indicate the shortest path by double lines (10 Marks) June/July2016
23.
Let p
and q are the two pixels at coordinates {100,120} and {130,160} respectively.
Compute i) Euclidean distance ii) Chess board distance and iii) Manhattan
distance (6 Marks) June/July2016
24.
With a
neat block diagram explain the fundamental steps involved in digital image
processing. (10 Marks) Dec2015/Jan 2016
25.
With
the neat diagram of the eye explain the human visual system working. (10 Marks)
Dec2015/Jan 2016
26.
What
is meant by path. Give the formula for calculating D4 and D8 distances. What is
the differences between D8 and Dm distance(10 Marks) Dec2015/Jan 2016
27.
For V=
{2,3,4}, compute the length of shortest 4, 8, m-path between p and q , in the
following fig
28.
Explain
spatial and grey level resolution. (04 Marks) Dec2015/Jan 2016
29.
With a
neat block diagram explain the fundamental steps involved in digital image
processing. (10 Marks) Dec2014/Jan 2015
30.
Explain
the importance of brightness adaption and discrimination in image processing. (06
Marks) Dec2014/Jan 2015
31.
Mention
the applications of image processing (05 Marks) Dec2014/Jan 2015
32.
How many would it take to transmit a
512x512 image with 256 grey levels at 300 baud rate (04 Marks) Dec2014/Jan 2015
33.
Explain
the process of image acquisition by
sensor arrays and sensor strips (04 Marks) Dec2014/Jan 2015
34.
Explain
the components of a general purpose image processing system. (10 Marks) June/July 2015
35.
How is
image formed in human eye. Explain with examples that the perceived brightness
is not a function of intensity (10 Marks) June/July
2015
36.
Explain
the process of image acquisition using circular sensor strip (06 Marks) June/July 2015
37.
With neat block diagram explain the steps in image
processing (10 Marks) Dec2013/Jan 2014
38.
Explain
the following terms applicable to image processing with necessary graph
i)
Brightness
adaptation
ii)
Weber
ratio
iii)
Mach
bands
(10 Marks) Dec2013/Jan 2014
39.
Describe
the following terms applied to image processing
i)
Neighbors
of pixel
ii)
Adjcency
of pixel
iii)
Digital
path
iv)
City
block distance measures (4
Marks) Dec2013/Jan 2014
|
MODULE
2: Spatial Domain: Some Basic Intensity Transformation Functions, Histogram Processing,
Fundamentals of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial
Filters
Frequency
Domain: Preliminary Concepts, The Discrete Fourier
Transform (DFT) of Two Variables, Properties of the 2-D DFT, Filtering in
the Frequency Domain, Image Smoothing and Image Sharpening Using Frequency
Domain Filters, Selective Filtering.
|
1.
For
the 2x2 orthogonal matrix A and image U
obtain the transformed image and basis images and inverse transformation (06
Marks) Dec2017/Jan 2018
2.
Explain
the following properties of unitary transform (06 Marks) Dec2017/Jan 2018
i)
Energy
conservation
ii)
Decorrelation
3.
Define
two-dimensional DCT. Explain its properties (08 Marks) Dec2017/Jan
2018
4.
Using
the core matrix H1 generate hadamard transform matrix H3 and explain four
properties of hadamard transform (10 Marks) Dec2017/Jan 2018
5.
Define
slant transform. Explain 4x4 slant transformation matrix. Explain any four
properties of slant transform (10 Marks) Dec2017/Jan
2018
6.
Explain
the following intensity transformation function with necessary graphs
i)
Image
negatives
ii)
Log
transformation
iii)
Power
law transformation (10 Marks) Dec2017/Jan 2018
7.
Perform
histogram equalization of an image whose pixel intensity distributions is given
in table
|
Grey levels
|
0
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
|
Number of pixels
|
790
|
1023
|
850
|
656
|
329
|
245
|
122
|
81
|
Construct the histogram of the image before
and after equalization (10 Marks) Dec2017/Jan 2018
8.
Explain
smoothing of images in frequency domain using
i)
Ideal
LPF
ii)
Butterworth
LPF (10 Marks) Dec2017/Jan 2018
9.
With
the help of block diagram, explain
homomorphic filtering approach for image enhancement (10 Marks) Dec2017/Jan 2018
10.
Define
1D unitary transform and mention its properties(06 Marks) June/July 2017
11.
Compute
2D DFT of a 4x4 grey scale image shown in Fig and the corresponding inverse
transform (08 Marks) June/July 2017
12.
Prove
that FFT*T=1, Where F is the DFT matrix (06 Marks) June/July 2017
13.
Write
the generation of NXN Hadamard transformmatrix by iterative rule. Mention its
advantages and its properties (08 Marks) June/July
2017
14.
What
are the properties of slant transform and also find forward slant transform and
inverse slant transform of U
15.
Define
one-dimensional DCT for N=4 obtain NXN cosine transform (06 Marks) June/July 2017
16.
Explain
the following image enhancement techniques, highlighting their area of
application.
i)
Intensity
level slicing
ii)
Power
– law transformation transform (06
Marks) June/July 2017
17.
Explain
the following image enhancement techniques, highlighting their area of
application.
i) Bit – plane slicing.
ii) AND and OR operation transform (06 Marks) June/July 2017
18.
Explain
the smoothing of images in frequency domain using:
Ideal low pass filter
Butterworth lowpass filter transform (06 Marks) June/July 2017
19.
With a
block diagram and equations, explain the homomorphic filtering. How dynamic
range compression and contrast enhancement is simultaneously achieved? transform (06 Marks) June/July 2017
20.
An
image U and transformed matrix A are given by
Obtain the transformed image V . Compare
the energy in U and V and give inference . (06 Marks) Dec2016/Jan 2017
21.
Show
that the cosine transform of a vector of N elements can be calculated in
O(NlogN) option via N point FFT. (08 Marks) Dec2016/Jan 2017
22.
Obtain
the Haar transform matrix for N=4 . (06 Marks) Dec2016/Jan 2017
23.
Give
any three properties of unitary transform. (06 Marks) June/July 2016
24.
Compute
2D DFT of a 4x4 grey scale image given by (04 Marks) June/July 2016
25.
For
the 2x2 matrix A and image U
Calculate the transformed image V and the
basis images. Also reconstruct the original image U by inverse transform (10
Marks) June/July 2016
26.
Generate
the Haar transform matrix for N=2 (08 Marks) June/July 2016
27.
Compute
the K-L transform of the following matrix
28.
Define
hadamard transform for N=8 (06 Marks) Dec2015/Jan
2016
29.
Define
two-dimensional DFT. Explain the following properties i) Symmetry, Unitary ii)
Periodic extensions iii) Conjugate symmetry (10 Marks) June/July 2015
30.
Generate
hadamard transform for n=3 from the core matrix (06 Marks) June/July 2015
31.
Define
two-dimensional DFT. Explain the following properties of 2-DFT. i) Translation
ii) rotation iii) distributivity and scaling iv) separability (10 Marks) Dec2014/Jan 2015
32.
What
are basis vectors? (04 Marks) Dec2014/Jan 2015
33.
Define
discrete sine transform and its inverse transform. Discuss any three properties
(08 Marks) Dec2014/Jan 2015
34.
35.
What
is the importance of image enhancement in image processing? Explain in brief
any two point processing techniques implemented in image processing.
36.
Explain
histogram equalization technique.
37.
What
is histogram matching? Explain the development and implementation of the
method.
38.
Highlight
the importance of histograms in image processing and develop a procedure to
perform histogram equalization.
39.
Explain
the following image enhancement techniques, highlighting their area of
application.
iii)
Intensity
level slicing
iv)
Power
– law transformation
40.
Explain
the following image enhancement techniques, highlighting their area of
application.
i) Bit – plane slicing.
ii) AND and OR operation
41.
Explain
the smoothing of images in frequency domain using:
Ideal low pass filter
Butterworth lowpass filter
42.
With a
block diagram and equations, explain the homomorphic filtering. How dynamic
range compression and contrast enhancement is simultaneously achieved?
43.
Discuss
homomorphic filtering.
44.
Explain
sharpening filters in the frequency domain
45.
Explain the basic concept
of spatial filtering in image enhancement and hence explain the importance of
smoothing filters and median filters.
|
MODULE 3: Restoration: Noise models, Restoration in the Presence of Noise Only using
Spatial Filtering and Frequency Domain Filtering, Linear,
Position-Invariant Degradations, Estimating the Degradation Function,
Inverse Filtering, Minimum Mean Square Error (Wiener) Filtering,
Constrained Least Squares Filtering.
|
1.
Explain
the model of image degradation/ restoration. List all noise probability density
functions and explain any three with necessary equations and graphs (10 Marks) Dec2017/Jan 2018
2.
Explain
inverse filtering and weiner filtering in image processing (10 Marks) Dec2017/Jan 2018
3.
Discuss
the important noise probability
density functions (10 Marks) June/July
2017
4.
Discuss
the various mean filters for restoration in the presence of noise only spatial
filtering (10 Marks) June/July 2017
5.
Write
an explanatory note on the following noise model
i)
Erlang
noise
ii)
Rayleigh
noise
iii)
Impulse
noise
iv)
Uniform
noise (06 Marks) Dec2016/Jan 2017
6.
Explain
band reject filter used in periodic noise reduction in frequency domain (06
Marks) Dec2016/Jan 2017
7.
Derive
an expression for the linear degradation model in presence of additive noise (10
Marks) Dec2016/Jan 2017
8.
Discuss
adaptive median filtering method for image restoration. Also give its
advantages (10 Marks) June/July 2016
9.
Derive
an expression for observed image when the degradations are linear, position
invariant (10 Marks) June/July 2016
10.
Explain
the model of image degradation/ restoration. (08 Marks) Dec2015/Jan 2016
11.
What
are the three methods of estimating degradation function. Explain each of them (12
Marks) Dec2015/Jan 2016
12.
Explain
the three methods to estimate the degradation function used for image restoration(10 Marks) June/July 2015
13.
What
is the issue with inverse filtering for restoring image. Explain with
appropriate equations how weiner filter take care of these issues(10 Marks) June/July 2015
14.
Explain
the model of image degradation/ restoration. List all noise probability density
functions and explain any three with necessary equations and graphs (10 Marks) Dec2014/Jan 2015
15.
Explain
inverse filtering and weiner filtering in image processing (10 Marks) Dec2014/Jan 2015
16.
Draw
and explain image degradation and restoration model(10 Marks) June/July 2014
17.
Discuss
various mean filters used in image restoration system(10 Marks) June/July 2014
18.
Explain
in brief the inverse filtering approach. List its limitation(10 Marks) June/July 2014
19.
Explain the basic model of
image restoration process. Also with necessary equations explain the most common
PDF in an image processing (10 Marks) Dec2013/Jan 2014
|
MODULE 4: Color Image Processing: Color Fundamentals, Color Models, Pseudocolor
Image Processing.
Wavelets: Background, Multiresolution Expansions.
Morphological Image Processing: Preliminaries, Erosion and Dilation, Opening and
Closing, The Hit-or-Miss Transforms, Some Basic Morphological Algorithms.
|
1.
Explain
briefly any two colour models. Write equations for converting RGB to HIS (10 Marks) Dec/Jan 2017
2.
Write
a note on pseudocolour image processing.
Explain intensity slicing of pseudocolour image processing (10 Marks) Dec/Jan 2017.
3.
Explain
the color characteristics to distinguish various colors. Write the trichromatic
coefficients interms of X, Y and Z (08 Marks) June/July 2017
4.
Draw
the block diagram of Gray level to colour transformations and explain it (08
Marks) June/July 2017
5.
Write
a note on CMY color (04 Marks) June/July
2017
6.
Briefly
explain any two color models used in color image processing (10 Marks) Dec/Jan 2016
7.
Develop
a procedure for converting
i)
RGB to
HSI model
ii)
HSI to
RGB model (10 Marks) Dec/Jan 2016
8.
Write
a note on pseudocolour image processing.
Explain intensity slicing of pseudocolour image processing (10 Marks) June/July 2016
9.
Describe
RGB color model with the help of
relevant diagrams. Write equations to convert RGB to CMY (10 Marks) Dec/Jan 2015
10.
Develop
a procedure for converting
i)
RGB to
HSI model
ii)
HSI to
RGB model (10 Marks) June/July 2015
11.
Write
a note on pseudocolour image processing.
Explain intensity slicing of pseudocolour image processing (10 Marks) June/July 2015
12.
Develop
a procedure for converting
RGB to HSI model
HSI to RGB model (10 Marks) Dec/Jan 2014
13.
Write
a note on pseudocolour image processing.
Explain intensity slicing of pseudocolour image processing (10 Marks) Dec/Jan 2014
|
MODULE 5: Segmentation: Point, Line, and Edge Detection, Thresholding, Region-Based Segmentation,
Segmentation Using Morphological Watersheds.
Representation
and Description: Representation,
Boundary descriptors.
|
1. Explain Point, Line, and Edge Detection
2. Describe about basic edge detection and its techniques
3.
Explain the
following Thresholding, and using edges
by using global thresholding
4.
Define Region
based segmentation and briefly describe region splitting and merging
5.
Explain in
detail about Segmentation Using Morphological Watersheds.
6. Briefly discuss about Representation, Boundary descriptors.
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