Digital Image processing -2 Marks-Questions and Answers
UNIT I
DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS
1. Define Image?
An
Image may be defined as a two dimensional function f (x,y) where x & y
are
spatial (plane) coordinates, and the amplitude of f at any pair of coordinates
(x,y)
is called intensity or gray level of the image at that point. When x,y and the
amplitude
values of f are all finite, discrete quantities we call the image as Digital
Image.
2. Define Image Sampling?
Digitization
of spatial coordinates (x,y) is called Image Sampling. To be suitable for
computer processing, an image function f(x,y) must be digitized both
spatially
and in magnitude.
3. Define Quantization ?
Digitizing
the amplitude values is called Quantization. Quality of digital image
is
determined to a large degree by the number of samples and discrete gray
levels
used in sampling and quantization.
4. What is Dynamic Range?
The
range of values spanned by the gray scale is called dynamic range of an image.
Image will have high contrast, if the dynamic range is high and image
will
have dull washed out gray look if the dynamic range is low.
5. Define Mach band effect?
The
spatial interaction of Luminance from an object and its surround creates a
Phenomenon
called the mach band effect.
6. Define Brightness?
Brightness
of an object is the perceived luminance of the surround. Two objects
with
different surroundings would have identical luminance but different brightness.
7. Define Tapered Quantization?
If
gray levels in a certain range occur frequently while others occurs rarely, the
quantization
levels are finely spaced in this range and coarsely spaced outside of
it.This
method is sometimes called Tapered Quantization.
8. What do you meant by Gray level?
Gray
level refers to a scalar measure of intensity that ranges from black to grays
and
finally to white.
9. Define Resolutions?
Resolution
is defined as the smallest number of discernible detail in an image.
Spatial
resolution is the smallest discernible detail in an image and gray level
resolution
refers to the smallest discernible change is gray level.
10. Write the M X N digital image in compact
matrix form?
f(x,y
)= f(0,0) f(0,1)………………f(0,N-1)
f(1,0)
f(1,1)………………f(1,N-1)
.
.
.
f(M-1)
f(M-1,1)…………f(M-1,N-1)
11. Write the expression to find the number
of bits to store a digital image?
The
number of bits required to store a digital image is
b=M X
N X k
When
M=N, this equation becomes
b=N^2k
12. What do you meant by Zooming of digital
images?
Zooming
may be viewed as over sampling. It involves the creation of new
pixel
locations and the assignment of gray levels to those new locations.
13. What do you meant by shrinking of
digital images?
Shrinking
may be viewed as under sampling. To shrink an image by one
half,
we delete every row and column. To reduce possible aliasing effect, it is a
good
idea to blue an image slightly before shrinking it.
14. Define the term Radiance?
Radiance
is the total amount of energy that flows from the light source, and
it is
usually measured in watts (w).
15. Define the term Luminance?
Luminance
measured in lumens (lm), gives a measure of the amount of
energy
an observer perceiver from a light source.
16.
What is Image Transform?
An
image can be expanded in terms of a discrete set of basis arrays
called
basis images. Unitary matrices can generate these basis images.
Alternatively,
a given NXN image can be viewed as an N^2X1 vectors. An image
transform
provides a set of coordinates or basis vectors for vector space.
17.
What are the applications of transform?
1) To
reduce band width
2) To
reduce redundancy
3) To
extract feature.
18.
Give the Conditions for perfect transform?
Transpose
of matrix = Inverse of a matrix. Orthoganality.
19.
What are the properties of unitary transform?
1)
Determinant and the Eigen values of a unitary matrix have unity magnitude
2)
The entropy of a random vector is preserved under a unitary Transformation
3)
Since the entropy is a measure of average information, this means information
is
preserved under a unitary transformation.
20.
Write the expression of one-dimensional discrete Fourier transforms?
Forward
transform
The
sequence of x(n) is given by x(n) = { x0,x1,x2,… xN-1}.
X(k)
= (n=0 to N-1) _ x(n) exp(-j 2* pi* nk/N) ; k= 0,1,2,…N-1
Reverse
transforms
X(n)
= (1/N) (k=0 to N-1) _ x(k) exp(-j 2* pi* nk/N) ; n= 0,1,2,…N-1
21.
Properties of twiddle factor?
1.
Periodicity
WN^(K+N)=
WN^K
2.
Symmetry
WN^(K+N/2)=
-WN^K
22.
Give the Properties of one-dimensional DFT
1.
The DFT and unitary DFT matrices are symmetric.
2.
The extensions of the DFT and unitary DFT of a sequence and their
inverse
transforms are periodic with period N.
3.
The DFT or unitary DFT of a real sequence is conjugate symmetric
about
N/2.
23.
Give the Properties of two-dimensional DFT
1.
Symmetric
2.
Periodic extensions
3.
Sampled Fourier transform
4. Conjugate
symmetry.
24.
What is cosine transform?
The
NXN cosine transform c(k) is called the discrete cosine transform and is
defined
as
C(k)
= 1/_N , k=0, 0 _ n _ N-1
= _
(2/N) cos (pi (2n+1)/2N 1_ k _ N-1, 0_ n _ N-1
25.
What is sine transform?
The
NXN sine transform matrix = (k,n) also called the discrete sine transform is
defined
as (k,n) = _(2/N+1) sin [ pi* (k+1) (n+1) / (N+1)] , 0_k, n_ N-1
26.
Write the properties of cosine transform?
1)
Real & orthogonal.
2)
Fast transform.
3)
Has excellent energy compaction for highly correlated data
27.
Write the properties of sine transform?
1)
Real, symmetric and orthogonal.
2)
Not the imaginary part of the unitary DFT.
3)
Fast transform.
28.
Write the properties of Hadamard transform?
1)
Hadamard transform contains any one value.
2) No
multiplications are required in the transform calculations.
3)
The no: of additions or subtractions required can be reduced from N^2 to
Nlog2N
4)
Very good energy compaction for highly correlated images.
29.
Define Haar transform?
The
Haar functions are defined on a continuous interval Xe [0,1] and for
K=0,1,…….
N-1.Where N=2^n. The integer k can be uniquely decomposed as
K=2^P+Q-1.
30.
Write the expression for Hadamard transforms
Hadamard
transform matrices Hn are NXN matrices where N=2^n , n=
1,2,3,…
is defined as Hn= Hn-1 * H1 = H1* Hn-1
= 1/
_ 2 Hn-1 Hn-1
H2 =
1 1
1 –1
31.
What are the properties of Haar transform.
1.
Haar transform is real and orthogonal.
2.
Haar transform is a very fast transform
3.
Haar transform has very poor energy compaction for images
4.
The basic vectors of Haar matrix sequency ordered.
32.
What are the Properties of Slant transform?
1.
Slant transform is real and orthogonal.
2.
Slant transform is a fast transform
3.
Slant transform has very good energy compaction for images
4.
The basic vectors of Slant matrix are not sequency ordered.
33.
Define of KL Transform?
KL
Transform is an optimal in the sense that it minimizes the mean square
error
between the vectors X and their approximations X^. Due to this idea of
using
the Eigenvectors corresponding to largest Eigen values. It is also known as
principal
component transform.
34.
Justify that KLT is an optimal transform.
Since
mean square error of reconstructed image and original image is minimum
and
the mean value of transformed image is zero so that uncorrelated.
35.Explain the term digital image.
The
digital image is an array of real or complex numbers that is
represented
by a finite no of bits.
36.Write any four applications of DIP.
(i).
Remote sensing
(ii).
Image transmission and storage for business application
(iii).
Medical imaging
(iv).
Astronomy
37.What is the effect of Mach band pattern.
The
intensity or the brightness pattern perceive a darker stribe in region D
and
brighter stribe in region B.This effect is called Mach band pattern or
effect.
38.Write down the properties of 2D fourier
transform.
- Separability
- Translation
- Periodicity and Conjugate property
- Rotation
- Distributivity and scaling
- Average value
- Convolution and Correlation
- Laplacian
39.Obtain the Hadamard transformation for N
= 4
N = 4 = 2n
=> n = 2
40.Write down the properties of Haar transform.
- Real and orthogonal
- Very fast transform
- Basis vectors are sequentially ordered
- Has fair energy compaction for image
- Useful in feature extraction,image coding and image analysis
problem
UNIT II
IMAGE
ENHANCEMENT TECHNIQUES
1. What is Image Enhancement?
Image
enhancement is to process an image so that the output is more suitable
for
specific application.
2. Name the categories of Image Enhancement
and explain?
The
categories of Image Enhancement are
1.
Spatial domain
2.
Frequency domain
Spatial
domain: It refers to the image plane, itself and it is based on direct
manipulation
of pixels of an image.
Frequency
domain techniques are based on modifying the Fourier transform of
an
image.
3. What do you mean by Point processing?
Image
enhancement at any Point in an image depends only on the gray level
at
that point is often referred to as Point processing.
4. Explain Mask or Kernels?
A
Mask is a small two-dimensional array, in which the value of the mask
coefficient
determines the nature of the process, such as image sharpening.
5. What is Image Negatives?
The
negative of an image with gray levels in the range [0, L-1] is obtained by
using
the negative transformation, which is given by the expression.
s =
L-1-r
Where
s is output pixel.
r is
input pixel.
6.Define Histogram?
The
histogram of a digital image with gray levels in the range [0, L-1] is a
discrete
function h (rk) = nk, where rk is the kth gray level and nk is the number of
pixels
in the image having gray level rk.
7. Define Derivative filter?
For a
function f (x, y), the gradient f at co-ordinate (x, y) is defined as the
vector_f
= _f/_x
_f/_y
_f =
mag (_f) = {[(_f/_x) 2 +(_f/_y) 2 ]} ½
8. Explain spatial filtering?
Spatial
filtering is the process of moving the filter mask from point to point in
an
image. For linear spatial filter, the response is given by a sum of products of
the
filter coefficients, and the corresponding image pixels in the area spanned by
the
filter mask.
9. Define averaging filters?
The
output of a smoothing, linear spatial filter is the average of the pixels
contain
in the neighborhood of the filter mask. These filters are called averaging
filters.
10. What is a Median filter?
The
median filter replaces the value of a pixel by the median of the gray
levels
in the neighborhood of that pixel.
11. What is maximum filter and minimum
filter?
The
100th percentile is maximum filter is used in finding brightest points in
an
image. The 0th percentile filter is minimum filter used for finding darkest
points
in an
image.
12. Define high boost filter?
High
boost filtered image is defined as
HBF=
A (original image)-LPF
=
(A-1) original image + original image –LPF
HBF=
(A-1) original image +HPF
13. State the condition of transformation
function s=T(r)
1.
T(r) is single-valued and monotonically increasing in the interval 0_r_1
0_T(r)
_1 for 0_r_1.
14. Write the application of sharpening filters?
1.
Electronic printing and medical imaging to industrial application
2.
Autonomous target detection in smart weapons.
15. Name the different types of derivative
filters?
1.
Perwitt operators
2.
Roberts cross gradient operators
3.
Sobel operators
16. What is enhancement.
Image
enhancement is a technique to process an image so that the result
is
more suitable than the original image for specific applications;
17. What is point processing.
Enhancement
at any point in an image depends only on the gray level at
that
point is referred to as point processing.
18. What is gray level slicing.
Highlighting
a specific range of gray levels in an image is referred to as
gray
level slicing. It is used in satellite imagery and x-ray images.
19. What is histogram equalization?
It is
a technique used to obtain linear histogram . It is also known as
histogram
linearization. Condition for uniform histogram is Ps(s) = 1.
20.
What is contrast stretching?
Contrast stretching reduces an image of
higher contrast than the original by darkening the levels below m and
brightening the levels above m in the image.
21.
Define image subtraction.
The difference between 2 images f(x,y)
and h(x,y) expressed as,
G(x,y)=f(x,y)-h(x,y) is obtained by computing the difference
between all pairs of corresponding pixels from f and h.
22.
What is the purpose of image averaging?
An important application of image
averaging is in the field of astronomy, where imaging with very low light
levels is routine, causing sensor noise frequently to render single images
virtually useless for analysis.
23.
What is meant by masking?
Mask is the small 2D array in which the
values of mask co-efficient determines the nature of process.
The enhancement techniques based on this
type of approach is referred to as mask processing.
24.
Give the formula for log transformation
S=clog(1+r)
Where c- constant and r≥0
25.
What is meant by bit plane slicing?
Instead of highlighting gray level
ranges, highlighting the contribution made to total image appearance by
specific bits might be desired. Suppose that each pixel in an image is
represented by 8 bits. Imagine that the image is composed of eight 1-bit
planes, ranging from bit plane 0 for LSB to bit plane 7 for MSB.
UNIT III
IMAGE
RESTORATION
1. Define Restoration?
Restoration
is a process of reconstructing or recovering an image that has
been
degraded by using a priori knowledge of the degradation phenomenon.
Thus
restoration techniques are oriented towards modeling the degradation and
applying
the inverse process in order to recover the original image.
2. How a degradation process is modeled?
A
system operator H, which together with an additive white noise term
_(x,y)
a operates on an input image f(x,y) to produce a degraded image g(x,y).
3. What is homogeneity property and what is
the significance of this
property?
H
[k1f1(x,y)] = k1H[f1(x,y)]
Where
H=operator
K1=constant
f(x,y)=input
image.
It
says that the response to a constant multiple of any input is equal to the
response
to that input multiplied by the same constant.
4. Define circulant matrix?
A
square matrix, in which each row is a circular shift of the preceding row
and
the first row is a circular shift of the last row, is called circulant matrix.
Example:
he(o) he(M-1)
he(M-2)………… he(1)
he(1) he(0)
he(M-1)………. he(2)
.
.
he(M-1) he(M-2)
he(M-3)………. he(0)
5. What is the concept behind algebraic
approach to restoration?
Algebraic
approach is the concept of seeking an estimate of f, denoted f^,
that
minimizes a predefined criterion of performance where f is the image.
6. Why the image is subjected to wiener
filtering?
This
method of filtering consider images and noise as random process
and
the objective is to find an estimate f^ of the uncorrupted image f such that
the
mean
square error between them is minimized. So that image is subjected to
wiener
filtering to minimize the error.
7. Define spatial transformation?
Spatial
transformation is defined as the rearrangement of pixels on an
image
plane.
8. Define Gray-level interpolation?
Gray-level
interpolation deals with the assignment of gray levels to pixels in
the
spatially transformed image.
9. Give one example for the principal source
of noise?
The
principal source of noise in digital images arise image acquisition
(digitization)
and/or transmission. The performance of imaging sensors is
affected
by a variety of factors, such as environmental conditions during image
acquisition
and by the quality of the sensing elements. The factors are light levels
and
sensor temperature.
10. When does the degradation model satisfy
position invariant property?
An
operator having input-output relationship g(x,y)=H[f(x,y)] is said to
position
invariant if H[f(x-,y-_)]=g(x-,y-_) for any f(x,y) and and _.
This
definition indicates that the response at any point in the image depends only
on
the value of the input at that point not on its position.
11. Why the restoration is called as
unconstrained restoration?
In
the absence of any knowledge about the noise ‘n’, a meaningful criterion
function
is to seek an f^ such that H f^ approximates of in a least square sense
by
assuming the noise term is as small as possible.
Where
H = system operator.
f^ =
estimated input image.
g =
degraded image.
12. Which is the most frequent method to
overcome the difficulty to
formulate the spatial relocation of pixels?
The
point is the most frequent method, which are subsets of pixels whose
location
in the input (distorted) and output (corrected) imaged is known precisely.
13. What are the three methods of estimating
the degradation function?
1.
Observation
2.
Experimentation
3.
Mathematical modeling.
14. How the blur is removed caused by
uniform linear motion?
An
image f(x,y) undergoes planar motion in the x and y-direction and x0(t)
and
y0(t) are the time varying components of motion. The total exposure at any
point
of the recording medium (digital memory) is obtained by integrating the
instantaneous
exposure over the time interval during which the imaging system
shutter
is open.
15. What is inverse filtering?
The
simplest approach to restoration is direct inverse filtering, an estimate
F^(u,v)
of the transform of the original image simply by dividing the transform of
the
degraded image, G^(u,v) by the degradation function.
F^
(u,v) = G^(u,v)/H(u,v)
16. Give the difference between Enhancement
and Restoration?
Enhancement
technique is based primarily on the pleasing aspects it might
present
to the viewer. For example: Contrast Stretching. Where as Removal of
image
blur by applying a deblurrings function is considered a restoration
technique.
17.Define the degradation phenomena?
Image
restoration or degradation is a process that attempts to reconstruct
or
recover an image that has been degraded by using some clear
knowledge
of the degradation phenomena. Degradation may be in the
form
of
- Sensor noise
- Blur due to camera misfocus
- Relative object camera motion
18.What is unconstrained restoration.
It is
also known as least square error approach.n = g-Hf
To
estimate the original image f^,noise n has to be minimized and
f^ =
g/H.
19.What is blind image restoration
Degradation
may be difficult to measure or may be time varying in an
unpredictable
manner. In such cases information about the degradation
must
be extracted from the observed image either explicitly or implicitly.
This
task is called blind image restoration.
20.
What are the 2 properties in Linear operator?
* Additivity
* Homogenity
21.Explain
additivity property in Linear Operator?
H[f1(x,y)+f2(x,y)]=H[f1(x,y)]+H[f2(x,y)]
22.What
are the 2 methods of algebraic approach?
* Unconstraint restoration approach
* Constraint restoration approach
23.
What is meant by Noise probability density function?
The spatial noise descriptor is the
statistical behavior of gray level values in the noise component of the model.
24.
What are the types of noise models?
- Guassian noise
- Rayleigh noise
- Erlang noise
25.
What is meant by least mean square filter?
The limitation of inverse and pseudo
inverse filter is very sensitive noise. The wiener filtering is a method of
restoring images in the presence of blur as well as noise.
26.
What are the 2 approaches for blind image restoration?
- Direct measurement
- Indirect estimation
UNIT IV
IMAGE
COMPRESSION
1.
What is Data Compression?
Data
compression requires the identification and extraction of source
redundancy.
In other words, data compression seeks to reduce the number of
bits
used to store or transmit information.
2.
What are two main types of Data compression?
Lossless
compression can recover the exact original data after compression. It
is
used mainly for compressing database records, spreadsheets or word
processing
files, where exact replication of the original is essential.
��_Lossy
compression will result in a certain loss of accuracy in exchange for a
substantial
increase in compression. Lossy compression is more effective when
used
to compress graphic images and digitised voice where losses outside visual
or
aural perception can be tolerated.
3.
What is the need for Compression?
In
terms of storage, the capacity of a storage device can be effectively increased
with
methods that compress a body of data on its way to a storage device and
decompresses
it when it is retrieved. In terms of communications, the bandwidth
of a
digital communication link can be effectively increased by compressed data
at
the sending end and decompressing data at the receiving end. At any given
time,
the ability of the Internet to transfer data is fixed. Thus, if data can
effectively
be compressed wherever possible, significant improvements of data
throughput
can be achieved. Many files can be combined into one compressed
document
making sending easier.
4.
What are different Compression Methods?
(1)
Run Length Encoding (RLE)
(2)
Arithmetic coding
(3)
Huffman coding
(4)
Transform coding
5.
What is run length coding?
Run-length
Encoding,( RLE) is a technique used to reduce the size of a
repeating
string of characters. This repeating string is called a run; typically
RLE
encodes
a run of symbols into two bytes, a count and a symbol. RLE can
compress
any type of data regardless of its information content, but the content
of
data to be compressed affects the compression ratio. Compression is normally
measured
with the compression ratio:
6.
Define compression ratio.
Compression
Ratio = original size / compressed size: 1
7.
Give an example for Run length Encoding.
Consider
a character run of 15 ’A’ characters, which normally would
require
15 bytes to store:
AAAAAAAAAAAAAAA
coded into 15A
With
RLE, this would only require two bytes to store; the count (15) is stored as
the
first byte and the symbol (A) as the second byte.
8.
What is Huffman Coding?
Huffman
compression reduces the average code length used to represent
the
symbols of an alphabet. Symbols of the source alphabet, which occur
frequently,
are assigned with short length codes. The general strategy is to allow
the
code length to vary from character to character and to ensure that the
frequently
occurring characters have shorter codes.
9.
What is Arithmetic Coding?
Arithmetic
compression is an alternative to Huffman compression; it
enables
characters to be represented as fractional bit lengths. Arithmetic coding
works
by representing a number by an interval of real numbers greater or equal
to
zero, but less than one. As a message becomes longer, the interval needed to
represent
it becomes smaller and smaller, and the number of bits needed to
specify
it increases.
10.
What is JPEG?
The
acronym is expanded as "Joint Photographic Expert Group". It is an
international
standard in 1992. It perfectly Works with colour and greyscale
images,
Many applications e.g., satellite, medical, etc,
11.
What are the basic steps in JPEG?
The
Major Steps in JPEG Coding involves
DCT
(Discrete Cosine Transformation)
Quantization
Zigzag
Scan
DPCM
on DC component
RLE
on AC Components
Entropy
Coding
12.
What is MPEG?
The
acronym is expanded as "Moving Picture Expert Group". It is an
international standard in 1992. It perfectly Works with video and also used in
teleconferencing.
13.
What is transform coding?
Transform
coding is used to convert spatial image pixel values to transform
coefficient
values. Since this is a linear process and no information is lost, the
number
of coefficients produced is equal to the number of pixels transformed.
The
desired effect is that most of the energy in the image will be contained in a
few
large transform coefficients. If it is generally the same few coefficients that
contain
most of the energy in most pictures, then the coefficients may be further
coded
by lossless entropy coding. In addition, it is likely that the smaller
coefficients
can be coarsely quantized or deleted (lossy coding) without doing
visible
damage to the reproduced image.
14.
What are the different transforms used in transform coding and how the
differ?
Many
types of transforms used for picture coding, are Fourier, Karhonen-Loeve,
Walsh
- Hadamard, lapped orthogonal, discrete cosine (DCT), and recently,
wavelets.
The various transforms differ among themselves in three basic ways
that
are of interest in picture coding:
1)
The degree of concentration of energy in a few coefficients;
2)
The region of influence of each coefficient in the reconstructed picture;
3)
The appearance and visibility of coding noise due to coarse quantization of the
coefficients.
15.Find the number of bits to store a 128_128 image with 64 gray levels.
Given:
M = N = 128
L =
64 =2k
=>
k=6
No.
of bits = M2k
=
1282*6
=
98304 bits
16.
What is image compression?
Image compression refers to the process
of redundancy amount of data required to represent the given quantity of
information for digital image. The basis of reduction process is removal of
redundant data.
17.
Define coding redundancy.
If the gray level of an image is coded in
a way that uses more code words than necessary to represent each gray level,
then the resulting image is said to contain coding redundancy.
18.
Define interpixel redundancy.
The value of any given pixel can be
predicted from the values of its neighbors. The information carried by is
small. Therefore the visual contribution of a single pixel to an image is
redundant. Otherwise called as spatial redundant geometric redundant or
interpixel redundant.
Example : Run length coding
19.
What is psycho visual redundancy?
In normal visual processing certain
information has less importance than other information. So this information is
said to be psycho visual redundant.
20.
Define encoder.
Source encoder is responsible for removing
the coding and interpixel redundancy and psycho visual redundancy.
There are 2 components
a)
Source Encoder
b)
Channel Encoder
21.
Define Source encoder.
Source encoder performs 3 operations
1)
Mapper this transforms the input data into non-visual
format. It reduces the interpixel redundancy.
2)
Quantizer- It reduces the psycho visual redundancy of
the input images. This step is omitted if the system is error free.
3)
Symbol encoder – This reduces the coding redundancy.
This is the final stage of encoding process.
22.
Define channel encoder
The channel encoder reduces the impact
of the channel noise by inserting redundant bits into the source encoded data.
Example : Hamming code
23.
What are the types of decoder?
Source
decoder has 2 components.
a)
Symbol decoder – This performs inverse operation of
symbol encoder.
b)
Inverse Mapping – This performs inverse operation of
mapper.
24.What
is Variable Length coding?
Variable
Length Coding is the simplest approach to error free compression. It reduces
only the coding redundancy. It assigns the shortest possible codeword to the
most probable gray levels.
25.
What are the operations performed by error free compression?
1) Devising an alternative representation of the
image in which its interpixel redundant are reduced.
2) Coding the representation to
eliminate coding redundancy.
UNIT V
IMAGE
SEGMENTATION AND REPRESENTATION
1. What is segmentation?
The
first step in image analysis is to segment the image. Segmentation
subdivides
an image into its constituent parts or objects.
2. Write the applications of segmentation.
(i)
Detection of isolated points.
(ii)
Detection of lines and edges in an image.
3. What are the three types of discontinuity
in digital image?
Points,
lines and edges.
4. How the discontinuity is detected in an
image using segmentation?
(i)
Compute the sum of the products of the coefficient with the gray levels
contained
in the region encompassed by the mask.
(ii)
The response of the mask at any point in the image is
R =
w1z1+ w2z2 + w3z3 +………..+ w9z9
Where
zi = gray level of pixels associated with mass coefficient wi.
(iii)
The response of the mask is defined with respect to its center
location.
5. Why edge detection is most common approach for detecting
discontinuities?
The
isolated points and thin lines are not frequent occurrences in most
practical
applications, so edge detection is mostly preferred in detection of
discontinuities.
6. How the derivatives are obtained in edge detection during formulation?
The
first derivative at any point in an image is obtained by using the
magnitude
of the gradient at that point. Similarly the second derivatives are
obtained
by using the laplacian.
7. Write about linking edge points.
The
approach for linking edge points is to analyse the characteristics of
pixels
in a small neighborhood (3x3 or 5x5) about every point (x,y)in an image
that
has undergone edge detection. All points that are similar are linked, forming
a
boundary of pixels that share some common properties.
8. What are the two properties used for establishing similarity of edge
pixels?
(1)
The strength of the response of the gradient operator used to produce
the
edge pixel.
(2)
The direction of the gradient.
W1 W2
W3
W4 W5
W6
W7 W8
W9
9. Explain about gradient operator.
The
gradient of an image f(x,y) at location (x,y) is the vector
_f =
GX = _f/_x
GY
_f/_y
-The
gradient vector points are in the direction of maximum rate of change of f at
(x,y)
- In
edge detection an important quantity is the magnitude of this vector
(gradient)
and is
denoted
as _f
_f =
mag (_f) = [Gx2+Gy2] 1/2
The
direction of gradient vector also is an important quantity.
(x,y)
= tan-1(Gy/Gx)
10. What is the advantage of using sobel
operator?
Sobel
operators have the advantage of providing both the differencing and
a
smoothing effect. Because derivatives enhance noise, the smoothing effect is
particularly
attractive feature of the sobel operators.
11.
What is pattern?
Pattern
is a quantitative or structural description of an object or some other
entity
of interest in an image. It is formed by one or more descriptors.
12.
What is pattern class?
It is
a family of patterns that share some common properties. Pattern classes
are
denoted as w1 w2 w3 ……… wM , where M is the number of classes.
13.
What is pattern recognition?
It
involves the techniques for arranging pattern to their respective classes
by
automatically and with a little human intervention.
14.
What are the three principle pattern arrangements?
The
three principal pattern arrangements are vectors, Strings and trees.
Pattern
vectors are represented by old lowercase letters such as x y z and
In
the form x=[x1, x2, ……….., xn ] Each component x represents I th descriptor
and n
is the number of such descriptor.
15.Name the types of connectivity and explain
(a).
4-connectivity:
Two
pixels p and q with values from V are 4-connected if q is in the set N4(p)
(b).
8- connectivity:
Two
pixels p and q with values from V are 8-connected if q is in the set N8(p)
(c).
m- connectivity:
Two
pixels p and q with values from V are m-connected if
(i).
q is in N4(p) or
(ii).
q is in ND(p) and the set N4(p) _N4(q) = _
16. Define the chessboard distance
It is
also known as D8 distance given by
D8
(p,q) = max(_x-s_,_y-t_)
The
pixels with D8 distance from (x,y) less than or equal to some value r
form
a square centered at (x,y).
17.
What is edge?
An edge is a set of connected pixels
that lie on the boundary between 2 regions edges are more closely modeled as
having a ramplike profile. The slope of the ramp is inversely proportional to
the degree of blurring in the edge.
18.
Give the properties of the second derivative around an edge?
* The sign of the second derivative can be
used to determine whether an edge pixel lies on the dark or light side of an
edge.
* It produces 2 values for every edge in an
image.
* An imaginary straight line joining the
extreme positive and negative values of the second derivative would cross zero
near the midpoint of the edge.
19.
What is meant by object point and background point?
To execute the objects from the background is
to select a threshold T that separate these modes. Then any point (x,y) for
which f(x,y)>T is called an object point. Otherwise the point is called the
background point.
20.
What is global, local and dynamic or adaptive threshold?
When Threshold T depends only on f(x,y)
then the threshold is called global. If T depends both on f(x,y) and p(x,y) is
called local. If T depends on the spatial coordinates x and y the threshold is
called dynamic or adaptive where f(x,y) is the original image.
21.
Define region growing?
Region growing is a procedure that
groups pixels or sub regions in to layer regions based on predefined criteria.
The basic approach is to start with a set of seed points and from there grow
regions by appending to each seed these neighbouring pixels that have
properties similar to the seed.
22.Specify
the steps involved in splitting and merging?
Split into 4 disjoint quadrants any region
Ri for which P(Ri)=FALSE
Merge any adjacent regions Rj and
Rk for which P(Rj URk)=TRUE.
Stop when no further merging or splitting
is positive.
23.
What is meant by markers?
An approach used to control over
segmentation is based on markers. Marker is a connected component belonging to
an image. We have internal markers associated with objects of interest and
external markers associated with background.
24.
What are the 2 principles steps involved in marker selection?
The 2 steps are
1.
Preprocessing
2.
Definition of a set of criteria that markers must
satisfy.
25.
Define Chain codes?
Chain codes are used to represent a
boundary by a connected sequence of straight line segment of specified length
and direction. Typically this representation is based on 4 or 8 connectivity of the segments. The
direction of each segment is coded by using a numbering scheme.
26.
Specify the various polygonal approximation methods.
- Minimum perimeter polygons
- Merging techniques
- Splitting techniques
27.
Name few boundary descriptors
- Simple descriptors
- Shape numbers
- Fourier descriptors
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