It 603

B.Tech-Information Technology
Sixth Semester
DIGITAL IMAGE PROCESSING (IT 603)
(2marks questions and answers)
PREPARED BY: S.H.KRISHNA VENI
L/IT DEPT.

UNIT I
PART- A
1. Define Image
An image may be defined as two dimensional light intensity function f(x, y)
where x and y denote spatial co-ordinate and the amplitude or value of f at any point
(x, y) is called intensity or grayscale or brightness of the image at that point.
2. 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.
3. 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.
5. 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.
6. What do you meant by Color model?
A Color model is a specification of 3D-coordinates system and a subspace within
that system where each color is represented by a single point.
7. List the hardware oriented color models
1. RGB model
2. CMY model
3. CMYK model
4. HSI model
8. What is Hue and saturation?
Hue is a color attribute that describes a pure color.
saturation gives a measure of the degree to which a pure color is diluted by
white light.
9. List the applications of color models
1. RGB model--- used for color monitors & color video camera
2. CMY model---used for color printing
3. HIS model----used for color image processing
4. YIQ model---used for color picture transmission
10. What is Chromatic Adoption?
` The hue of a perceived color depends on the adoption of the viewer. For example,
the American Flag will not immediately appear red, white, and blue of the viewer has

been subjected to high intensity red light before viewing the flag. The color of the flag
will appear to shift in hue toward the red component cyan.
11. 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.
12. What is meant by pixel?
A digital image is composed of a finite number of elements each of which has a
particular location or value. These elements are referred to as pixels or image elements or
picture elements or pels elements.
13. Define Digital image
When x, y and the amplitude values of f all are finite discrete quantities , we call
the image digital image.
14. What are the steps involved in DIP?
1. Image Acquisition
2. Preprocessing
3. Segmentation
4. Representation and Description
5. Recognition and Interpretation
15. What is recognition and Interpretation?
Recognition means is a process that assigns a label to an object based on the
information provided by its descriptors.
Interpretation means assigning meaning to a recognized object.
16. Specify the elements of DIP system
1. Image Acquisition
2. Storage
3. Processing
4. Display
17. List the categories of digital storage
1. Short term storage for use during processing.
2. Online storage for relatively fast recall.
3. Archival storage for infrequent access.
18. What are the types of light receptors?
The two types of light receptors are
Cones and
Rods

19. Differentiate photopic and scotopic vision
Photopic vision Scotopic vision
1. The human being can resolve
the fine details with these cones
because each one is connected to
its own nerve end.
2. This is also known as bright
light vision.
Several rods are connected to
one nerve end. So it gives the
overall picture of the image.
This is also known as thin light
vision.
20. How cones and rods are distributed in retina?
In each eye, cones are in the range 6-7 million and rods are in the range 75-150
million.
21. Define subjective brightness and brightness adaptation
Subjective brightness means intensity as preserved by the human visual system.
Brightness adaptation means the human visual system can operate only from
scotopic to glare limit. It cannot operate over the range simultaneously. It accomplishes
this large variation by changes in its overall intensity.
22. Define weber ratio
The ratio of increment of illumination to background of illumination is called as
weber ratio.(ie) Δi/i
If the ratio (Δi/i) is small, then small percentage of change in intensity is needed
(ie) good brightness adaptation.
If the ratio (Δi/i) is large , then large percentage of change in intensity is needed
(ie) poor brightness adaptation.
23. What is meant by machband effect?
Machband effect means the intensity of the stripes is constant. Therefore it
preserves the brightness pattern near the boundaries, these bands are called as machband
effect.
24. What is simultaneous contrast?
The region reserved brightness not depend on its intensity but also on its
background. All centre square have same intensity. However they appear to the eye to
become darker as the background becomes lighter.
25. What is meant by illumination and reflectance?
Illumination is the amount of source light incident on the scene. It is represented
as i(x, y).
Reflectance is the amount of light reflected by the object in the scene. It is
represented by r(x, y).
26. Define sampling and quantization

Sampling means digitizing the co-ordinate value (x, y).
Quantization means digitizing the amplitude value.
27. Find the number of bits required to store a 256 X 256 image with 32 gray levels
32 gray levels = 25
= 5 bits
256 * 256 * 5 = 327680 bits.
28. 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
29. Write short notes on neighbors of a pixel.
The pixel p at co-ordinates (x, y) has 4 neighbors (ie) 2 horizontal and 2 vertical
neighbors whose co-ordinates is given by (x+1, y), (x-1,y), (x,y-1), (x, y+1). This is
called as direct neighbors. It is denoted by N4(P)
Four diagonal neighbors of p have co-ordinates (x+1, y+1), (x+1,y-1), (x-1, y-1),
(x-1, y+1). It is denoted by ND(4).
Eight neighbors of p denoted by N8(P) is a combination of 4 direct neighbors and
4 diagonal neighbors.
30. Explain the types of connectivity.
1. 4 connectivity
2. 8 connectivity
3. M connectivity (mixed connectivity)
31. What is meant by path?
Path from pixel p with co-ordinates (x, y) to pixel q with co-ordinates (s,t) is a
sequence of distinct pixels with co-ordinates.
32. Give the formula for calculating D4 and D8 distance.
D4 distance ( city block distance) is defined by
D4(p, q) = |x-s| + |y-t|
D8 distance(chess board distance) is defined by
D8(p, q) = max(|x-s|, |y-t|).
33. What is geometric transformation?
Transformation is used to alter the co-ordinate description of image.
The basic geometric transformations are
1. Image translation
2. Scaling
3. Image rotation
34. What is image translation and scaling?
Image translation means reposition the image from one co-ordinate location to
another along straight line path.

Scaling is used to alter the size of the object or image (ie) a co-ordinate system is
scaled by a factor.
35. Define the term Luminance
Luminance measured in lumens (lm), gives a measure of the amount of energy an
observer perceiver from a light source.
UNIT II
1. Specify the objective of image enhancement technique.
The objective of enhancement technique is to process an image so that the result is
more suitable than the original image for a particular application.
2. List the 2 categories of image enhancement.
Spatial domain refers to image plane itself & approaches in this category are
based on direct manipulation of picture image.
Frequency domain methods based on modifying the image by fourier transform.
3. 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.
4. What is meant by masking?
Mask is the small 2-D array in which the values of mask co-efficient determines
the nature of process.
The enhancement technique based on this type of approach is referred to as mask
processing.
5. Give the mask used for high boost filtering.
6. What is meant by laplacian filter?
The laplacian for a function f(x,y) of 2 variables is defined as,
2 2 2 2 2
▼f = ∂ f / ∂ x + ∂ f / ∂ y
-1 -1 -1
-1 A+8 -1
-1 -1 -1
0 -1 0
-1 A+4 -1
0 -1 0

7. Write the steps involved in frequency domain filtering.
1. Multiply the input image by (-1) to center the transform.
2. Compute F(u,v), the DFT of the image from (1).
3. Multiply F(u,v) by a filter function H(u,v).
4. Compute the inverse DFT of the result in (3).
5. Obtain the real part of the result in (4).
6. Multiply the result in (5) by (-1)
8. Differentiate linear spatial filter and non-linear spatial filter.
s.no. Linear spatial filter Non-linear spatial filter
1.
2.
Response is a sum of products of
the filter co-efficient.
R = w(-1,-1) f(x-1,y-1) +
w(-1,0) f(x-1,y) + … +
w(0,0) f(x,y) + … +
w(1,0) f(x+1,y) +
w(1,1) f(x+1,y+1).
They do not explicitly use co-
efficients in the sum-of-products.
R = w1z1 + w2z2 + … +w9z9
9
= ∑ wizi
i=1
9. Define histogram.
The histogram of a digital image with gray levels in the range [0, L-1] is a
discrete function h(rk)=nk.
rk-kth gray level
nk- no.of pixels having gray value rk.
10.Specify the concept of Spatial domain methods.
Spatial domain methods are procedures that operate directly on the pixels ,
g(x,y)=T[f(x,y)]
f(x,y) input image
g(x,y) output image
T operator on f, defined over some neighborhood of x,y.
11. 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.
12. 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
]} 1/2

13. Define 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.
14. 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.
15. 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.
16. Write the application of sharpening filters
1. Electronic printing and medical imaging to industrial application
2. Autonomous target detection in smart weapons.
17. Write the equation for 1-D DFT.
F(u)=1/N x=0∑N-1
f(x)exp[-j2πux/N] for u=0,1,2,…….N-1------------------(1)
f(x)= u=0∑N-1
F(u)[j2πux/N], for x=0,1,2,…….N-1--------------------------(2)
Equations (1) and (2) called Discrete Fourier transform pair
The values u=0,1,2,………N-1 in the discrete Fourier transform corresponds to the
samples of the continuous transform at values 0, ∆u, 2∆u….(N-1)∆u.
18. Define Fast Fourier Transform
The Fourier transform of f(x) denoted by F(u) is defined by
F(u)= ∫ f(x) e-j2πux
dx ----------------(1)
-
The inverse fourier transform of f(x) is defined by
f(x)= ∫F(u) ej2πux
dx --------------------(2)
-
The equations (1) and (2) are known as fourier transform pair
19. Write the equations for 1-D Discrete Cosine Transform (DCT)
I-D DCT is defined as
C(u)= α(u) x=0∑N-1
f(x)cos[(2x+1)uπ/2N] for u=0,1,2,…….N-1
Inverse DCT is defined as
f(x)= u=0∑N-1
α(u)C(u)cos[(2x+1)uπ/2N] for x=0,1,2,…….N-1
In both cases α(u)=1/√N for u=0 and √2/√N for u=1,2,…….N-1
20. Write the equations for 2-D Discrete Cosine Transform (DCT)
C(u,v)= α(u) α(v) x=0∑N-1
y=0∑N-1
f(x,y)cos[(2x+1)uπ/2N]cos[(2y+1)vπ/2N]

for u,v=0,1,2,…….N-1
Inverse DCT is defined as
f(x,y)=u=0∑N-1
v=0∑N-1
α(u) α(v)C(u,v)cos[(2x+1)uπ/2N] cos[(2y+1)uπ/2N]
for x,y=0,1,2,…….N-1
In both cases α(u)=1/√N for u=0 and √2/√N for u=1,2,…….N-1
21. What is Image Transform?
An image can be expanded in terms of a discrete set of basis arrays called basis
images. These basis images can be generated by unitary matrices. 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.
22. Define Fourier spectrum and spectral density
Fourier spectrum is defined as
F(u) = |F(u)| e jφ(u)
Where
|F(u)| = R2
(u)+I2
(u)
φ(u) = tan-1
(I(u)/R(u))
Spectral density is defined by
p(u) = |F(u)|2
p(u) = R2
(u)+I2
(u)
23. Specify the properties of 2D Fourier transform.
The properties are
Separability
Translation
Periodicity and conjugate symmetry
Rotation
Distributivity and scaling
Average value
Laplacian
Convolution and correlation
sampling
24. 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.
UNIT III
1. What is segmentation?

Segmentation subdivides on image in to its constitute regions or objects. The level
to which the subdivides is carried depends on the problem being solved .That is
segmentation should when the objects of interest in application have been isolated.
2. 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.
3. Write about linking edge points.
The approach for linking edge points is to analyze 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.
4. 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.
6. What is edge?
An edge isa set of connected pixels that lie on the boundary between two 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.
7. 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 two values for every edge in an image.
An imaginary straightline joining the extreme positive and negative values of the
second derivative would cross zero near the midpoint of the edge.
8. Define Gradient Operator?
First order derivatives of a digital image are based on various approximation of
the 2-D gradient. The gradient of an image f(x,y) at location(x,y) is defined as the vector
Magnitude of the vector is
∆f=mag( ∆f )=[Gx2
+ Gy2
]1/2
∞(x,y)=tan-1
(Gy/Gx)
∞(x,y) is the direction angle of vector ∆f
9. 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 background point.
10. 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.
11. Define region growing?
Region growing is a procedure that groups pixels or subregions 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.
12. 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(RjURk)=TRUE.
Stop when no further merging or splitting is positive.
13. Write the Basic Formulation of region based segmentation.
Let Represent the region of image. We may view segmentation as a process that
partition R into n subregions,R1,R2,………………,such that
n
(a) ΣRi=R
i=1
(b) Ri is a connected region, i=1,2,…………..n.
(c) Ri∩Rj=Фfor all i and j,i≠j.
(d) P(Ri)=TRUE for i=1,2,……………………n.
(e) P(RiURj)=FALSE for i≠j.
14.Write the Laplacian equation.
The laplacian of a 2-D function f(x,y) is a second order derivatives defined as
▼²ƒ=∂²ƒ/∂²x+∂²ƒ/∂²y
The first laplacian is combined with smoothing as a precursor to finding edges via
zero crossings. Consider the function.
▼² ƒ=8z5-(z1+z2+z3+z4+z6+z7+z8+z9)
0 -1 0
-1 4 -1
0 -1 0
15. Write the properties used for establishing similarity of edge pixels
The strength of the response of the gradient operator used to produce the edge
pixel if |▼f(x,y) - ▼(x’,y’)|<=T where T is a nonnegative threshold.
The direction of the gradient.
α(x,y)=tanˉ¹ gy/gx
16.Define Zero crossing property
An imaginary straight line joining the extreme positive and negative values of the
sesecond derivatives would cross near the midpoint of the edge.
It is useful for locating the centers of thick edges.

17.Define Graph
A Graph G=(N,U) is a finite, non empty set of nodes N, together with a set U of
unordered pairs of distinct elements of N.
18.what is Multilevel Thresholding?
Classifies a point (x,y) as belonging to one object class if T1<f(x,y)<=T2,to the
other object class if fx,y)>T2,and to the background if f(x,y)<=T1.
19. What is adaptive Thresholding?
Divide the image in to subimages and then utilize a different threshold to segment
each subimage.Since the threshold used for each pixel depends on the location of pixel
interms of the subimages, his type of thresholding is adaptive.
20. Write the procedure steps for Houghman transform.
Compute the gradient of an image and threshold it to obtain a binary image.
Specify subdivisions in the parameter plane.
Examine the counts of the accumulator cells for a high pixel concentrations.
Examine the relationships between pixels in a chosen cell.
Unit-IV
1. 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.
2. 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.
3. 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.
4. 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 compressing 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.
5. What are different Compression Methods?
Run Length Encoding (RLE)
Arithmetic coding
Huffman coding and
Transform coding
6. Define is 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.
7. 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.
Eg: Run length coding
8. What is run length coding?
Run-length Encoding, or 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.
9. Define compression ratio.
Compression is normally measured with the compression ratio:
Compression Ratio = original size / compressed size: 1
10. Define 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.
11. Define encoder
Source encoder is responsible for removing the coding and interpixel redundancy
and psycho visual redundancy.
There are two components
A) Source Encoder
B) Channel Encoder
12. Define source encoder
Source encoder performs three 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.
13. Define channel encoder
The channel encoder reduces reduces the impact of the channel noise by inserting
redundant bits into the source encoded data.
Eg: Hamming code
14. What are the types of decoder?
Source decoder- has two components
a) Symbol decoder- This performs inverse operation of symbol encoder.
b) Inverse mapping- This performs inverse operation of mapper.
Channel decoder-this is omitted if the system is error free.
15. 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
16. 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.
17. Define Huffman coding
Huffman coding is a popular technique for removing coding redundancy.
When coding the symbols of an information source the Huffman code yields
the smallest possible number of code words, code symbols per source symbol.
18. Define Block code
Each source symbol is mapped into fixed sequence of code symbols or code
words. So it is called as block code.
19. Define instantaneous code
A code word that is not a prefix of any other code word is called instantaneous or
prefix codeword.
20. Define arithmetic coding
In arithmetic coding one to one corresponds between source symbols and code
word doesn’t exist where as the single arithmetic code word assigned for a sequence of
source symbols. A code word defines an interval of number between 0 and 1.
21. What is bit plane Decomposition?

An effective technique for reducing an image’s interpixel redundancies is to
process the image’s bit plane individually. This technique is based on the concept of
decomposing multilevel images into a series of binary images and compressing each
binary image via one of several well-known binary compression methods.
22. Draw the block diagram of transform coding system
23. How effectiveness of quantization can be improved?
Introducing an enlarged quantization interval around zero, called a deadzero.
Adapting the size of the quantization intervals from scale to scale. In either
case, the selected quantization intervals must be transmitted to the decoder with the
encoded image bit stream.
24. What are the coding systems in JPEG?
A lossy baseline coding system, which is based on the DCT and is adequate for
most compression application.
An extended coding system for greater compression, higher precision or
progressive reconstruction applications.
a lossless independent coding system for reversible compression.
25. What is JPEG?
The acronym is expanded as "Joint Photographic Expert Group". It is an
international standard in 1992. It perfectly Works with color and grayscale images, Many
applications e.g., satellite, medical..
26. What are the basic steps in JPEG?
The Major Steps in JPEG Coding involve:
DCT (Discrete Cosine Transformation)
Quantization
Zigzag Scan
DPCM on DC component
RLE on AC Components
Entropy Coding
Input image Wavelet transform Quantizer Symbol
encoder
Symbol
decoder
Inverse wavelet
transform
Compressed
image
Compressed image
Decompressed
image

27. 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.
28. Draw the JPEG Encoder.
29. Draw the JPEG Decoder.
30. What is zig zag sequence?
The purpose of the Zig-zag Scan:
To group low frequency coefficients in top of vector.
Maps 8 x 8 to a 1 x 64 vector
31. Define I-frame

I-frame is Intraframe or Independent frame. An I-frame is compressed
independently of all frames. It resembles a JPEG encoded image. It is the reference point
for the motion estimation needed to generate subsequent P and P-frame.
32. Define P-frame
P-frame is called predictive frame. A P-frame is the compressed difference
between the current frame and a prediction of it based on the previous I or P-frame
33. Define B-frame
B-frame is the bidirectional frame. A B-frame is the compressed difference
between the current frame and a prediction of it based on the previous I or P-frame or
next P-frame. Accordingly the decoder must have access to both past and future reference
frames.
UNIT-V
1.Define Image Fusion
Combine higher spatial information in one band with higher spectral information
in another dataset to create ‘synthetic’ higher resolution multispectral datasets and images
2.Define Image Classification
The intent of the classification process is to categorize all pixels in a digital image
into one of several land cover classes, or "themes".
This categorized data may then be used to produce thematic maps of the land
cover present in an image.
3.What is the objective of Image Claasification?
The objective of image classification is to identify and portray, as a unique gray
level (or color), the features occurring in an image in terms of the object or type of land
cover these features actually represent on the ground.
4. Define pattern.
A pattern is a quantitative or structural description of an objective or some other entity
of interest in an image,
5. Define pattern class.
A pattern class is a family of patterns that share some common properties .Pattern
classes are denoted w1,w2,----wm, where M is the number of classes .
6. Define training pattern and training set.
The patterns used to estimate the parameters are called training patterns,anda set
of such patterns from each class is called a training set.
7. Define training
The process by which a training set is used to obtain decision functions is called
learning or training.
8.What is supervised claaification?

With supervised classification, we identify examples of the Information classes
(i.e., land cover type) of interest in the image. These are called "training sites". The
image processing software system is then used to develop a statistical characterization of
the reflectance for each information class.
9.What is Unsupervised classification?
Unsupervised classification is a method which examines a large number of
unknown pixels and divides into a number of classed based on natural groupings present
in the image values.
10. What is difference image ?
A difference image d(i,j) is a binary image where nonzero values represent image
areas with motion,that is areas where there was a substantial difference between gray
levels in consecutive images f1 and f2
d(i,j) = 0 if |f1(i,j)-f2(i,j)| <є
= 1 otherwise.
11. What is point distribution model?
The point distribution model is a powerful shape description technique that may
be used in locating new instances of such shapes in other images.
12.What are the various Image understanding control strategies?
Parallel and processing control
Hierarchical control
Bottom-up control
Mode-based control
Combined control
Non-Hierarchical control
13. Write the procedure for the Non-Hierarchical control.
1. Based on the actual state and acquired information about the solved problem,
decide on the best action and execute it.
2. Use the results of the last action to increase the amount of acquired information
about the problem.
3. If the goals of the task are met, stop. Otherwise return to step1.
14.State some of the motion related problems.
Motion detection
Moving object detection and location
Derivation of 3-D object properties
15. Define classifier
A classifier partitions feature space X into class-labeled regions such that
The classification consists of determining to which region a feature vector x belongs to.
Borders between decision boundaries are called decision regions.
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16. State the representation of classifier.
A classifier is typically represented as a set of discriminant functions
The classifier assigns a feature vector x to the i-the class if
17. What are the components of pattern recognition System
• Sensors and preprocessing.
• A feature extraction aims to create discriminative features good for
classification.
• A classifier.
• A teacher provides information about hidden state -- supervised learning.
• A learning algorithm sets PR from training examples.
18.What are the various approaches for pattern recognition?
Statistical PR: based on underlying statistical model of patterns and pattern
classes.
Structural (or syntactic) PR: pattern classes represented by means of formal
structures as grammars, automata, strings, etc.
Neural networks: classifier is represented as a network of cells modeling
neurons of the human brain (connectionist approach).
19.What is pseudo color image processing?
Pseudocolor image processing consists of assigning color to gray values based on
specified criterion.
The principal use of pseudocolor is for human visualization and interpretation of
gray scale events in an image or sequences of image.
20. What is intensity slicing?
If an image is interpreted as a 3-D function, the method can be viewed as one of
placing planes parallel to the coordinate plane;each plane then slices the function in the
area of intersection.
21. What are called Color complements?
The hues directty opposite one another on thecolor circle are called complements.
It is useful for enhancing detail that is embedded in the dark regions of a color
image-particularly when the regions are dominant in size.
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