Digital Image Processing 16 Marks for All Units
IN previous posts ,we added Two Marks for All 5 Units,here in this post we have added 16 marks questions and answers type for Digital Image Processing (DIP).
If you lik this posts/our works,It will be helpful if you share this to your frieds @facebook,@Twitter.
You may need to look :
* Two Marks Question and Answers :
- Unit 1
- Unit 2
- Unit 3
- Unit 4
- Unit 5
Have any queries ? ask it via comments.
16 MARKS for Digital Image Processing EC2029
UNIT I
1. Explain the steps involved in digital image processing.
(or)
Explain various functional block of digital image processing
# Image acquisition
# Preprocessing
# Segmentation
# Representation and Description
# Recognition and Interpretation
2. Describe the elements of visual perception.
# Cornea and Sclera
# Choroid – Iris diaphragm and Ciliary body
# Retina- Cones and Rods
3. Describe image formation in the eye with brightness adaptation and
discrimination
# Brightness adaptation
# Subjective brightness
# Weber ratio
#Mach band effect
#simultaneous contrast
4. Write short notes on sampling and quantization.
# Sampling
# Quantization
# Representing Digital Images
5. Describe the functions of elements of digital image processing system
with a diagram.
# Acquisition
# Storage
# Processing
# Communication
# Display
6. Explain the basic relationships between pixels?
# Neighbors of a pixel
# Connectivity, Adjacency, Path
# Distance Measure
# Arithmetic and Logic Operations
7. Explain the properties of 2D Fourier Transform.
# Separability
# Translation
# Periodicity and Conjugate Symmetry
# Rotation
# Distribution and Scaling
# Average Value
# Laplacian
# Convolution and correlation
# Sampling
8. ( i )Explain convolution property in 2D fourier transform.
* 1D Continuous
* 1D Discrete
* 1D convolution theorem
* 2D continuous
* 2D Discrete
* 2D convolution theorem
(ii) Find F (u) and |F (u)|
9. Explain Fast Fourier Transform (FFT) in detail.
# FFT Algorithm
# FFT Implementation
10. Explain in detail the different separable transforms
# Forward 1D DFT & 2D DFT
# Inverse 1D DFT & 2D DFT
# Properties
11. Explain Hadamard transformation in detail.
# 1D DHT
# 1D Inverse DHT
# 2D DHT
# 2D Inverse DHT
12. Discuss the properties and applications of
1)Hadamard transform 2)Hotelling transform
# Properties of hadamard:
Real and orthogonal
fast transform
faster than sine transform
Good energy compaction for image
# Appl:
Image data compression,
filtering and design of course
# Properties of hotelling:
Real and orthogonal
Not a fast transform
Best energy compaction for image
# Appl:
Useful in performance evaluation & for finding performance
Bounds
13. Explain Haar transform in detail.
# Def P= 2P+q-1
# Find h k (z)
14. Explain K-L transform in detail.
Consider a set of n or multi-dimensional discrete signal represented as column
vector x1,x2,…xn each having M elements,
X1
X2
X= .
.
Xn
The mean vector is defined as Mx=E{x}
Where E{x} is the expected value of x. M
For M vector samples mean vector is Mx=1/M _ Xk
K=1
T
The co-variant matrix is, Cx=E{(X-Mx)(X-Mx)}
M T
For M samples, Cx=1/M _ (xk-Mx)(xk-Mx).
K=1
K-L Transform Y= A (X- MX)
UNIT II
1. Explain the types of gray level transformation used for image enhancement.
# Linear (Negative and Identity)
# Logarithmic( Log and Inverse Log)
# Power_law (nth root and nth power)
# Piecewise_linear (Constrast Stretching, Gray level Slicing,
Bit plane Slicing)
2. What is histogram? Explain histogram equalization.
# P(rk) = nk/n
# Ps(s) = 1 means histogram is arranged uniformly.
3. Discuss the image smoothing filter with its model in the spatial domain.
# LPF-blurring
# Median filter – noise reduction & for sharpening image
4. What are image sharpening filters. Explain the various types of it.
# used for highlighting fine details
# HPF-output gets sharpen and background becomes darker
# High boost- output gets sharpen but background remains unchanged
# Derivative- First and Second order derivatives
Appl:
# Medical image
# electronic printing
# industrial inspection
5. Explain spatial filtering in image enhancement.
# Basics
# Smoothing filters
# Sharpening filters
6. Explain image enhancement in the frequency domain.
# Smoothing filters
# Sharpening filters
# Homomorphic filtering
7. Explain Homomorphic filtering in detail.
# f(x, y) = i(x, y) . r(x, y)
# Calculate the enhanced image g(x,y)
UNIT III
1. Explain the algebra approach in image restoration.
# Unconstrained
# Constrained
2. What is the use of wiener filter in image restoration. Explain.
# Calculate f^
# Calculate F^(u, v)
3. What is meant by Inverse filtering? Explain.
# Recovering i/p from its o/p
# Calculate f^(x, y)
4. Explain singular value decomposition and specify its properties.
# U= m=1_r___m _m
T
This equation is called as singular value decomposition of an image.
# Properties
The SVD transform varies drastically from image to image.
The SVD transform gives best energy packing efficiency for any given
image.
The SVD transform is useful in the design of filters finding least
square,minimum solution of linear equation and finding rank of large
matrices.
5. Explain image degradation model /restoration process in detail.
# Image degradation model /restoration process diagram
# Degradation model for Continuous function
# Degradation model for Discrete function – 1_D and 2_D
6. What are the two approaches for blind image restoration? Explain in detail.
_ Direct measurement
_ Indirect estimation
UNIT IV
1. What is data redundancy? Explain three basic data redundancy?
Definition of data redundancy
The 3 basic data redundancy are
_ Coding redundancy
_ Interpixel redundancy
_ Psycho visual redundancy
2. What is image compression? Explain any four variable length coding
compression schemes.
Definition of image compression
Variable Length Coding
* Huffman coding
* B2 Code
* Huffman shift
* Huffman Truncated
* Binary Shift
*Arithmetic coding
3. Explain about Image compression model?
The source Encoder and Decoder
The channel Encoder and Decoder
4. Explain about Error free Compression?
a. Variable Length coding
i. Huffman coding
ii. Arithmetic coding
b. LZW coding
c. Bit Plane coding
d. Lossless Predictive coding
5. Explain about Lossy compression?
Lossy predictive coding
Transform coding
Wavelet coding
6. Explain the schematics of image compression standard JPEG.
Lossy baseline coding system
Extended coding system
Lossless Independent coding system
7. Explain how compression is achieved in transform coding and explain about DCT
_ Block diagram of encoder
_ decoder
_ Bit allocation
_ 1D transform coding
_ 2D transform coding, application
_ 1D,2D DCT
8. Explain arithmetic coding
_ Non-block code
_ One example
9. Explain about Image compression standards?
_ Binary Image compression standards
_ Continuous tone still Image compression standards
_ Video compression standards
10. Discuss about MPEG standard and compare with JPEG
_ Motion Picture Experts Group
1. MPEG-1
2. MPEG-2
3. MPEG-4
_ Block diagram
_ I-frame
_ p-frame
_ B-frame
UNIT V
1. What is image segmentation. Explain in detail.
Definition - image segmentation
Discontinity – Point, Line, Edge
Similarity – Thresholding, Region Growing, Splitting and
Merging
2. Explain Edge Detection in details?
* Basic formation.
* Gradient Operators
* Laplacian Operators
3. Define Thresholding and explain the various methods of thresholding in detail?
Foundation
The role of illumination
Basic adaptive thresholding
Basic adaptive thresholding
Optimal global & adaptive thresholding.
4. Discuss about region based image segmentation techniques. Compare
threshold region based techniques.
* Region Growing
* Region splitting and merging
* Comparison
5. Define and explain the various representation approaches?
chain codes
Polygon approximations
Signature
Boundary segments
Skeletons.
6. Explain Boundary descriptors.
Simple descriptors.
Fourier descriptors.
7. Explain regional descriptors
Simple descriptors
Texture
i. Statistical approach
ii. Structural approach
iii. Spectral approach
8. Explain the two techniques of region representation.
_ Chain codes
_ Polygonol approximation
9. Explain the segmentation techniques that are based on finding the regions
directly.
_ Edge detection line detection
_ Region growing
_ Region splitting
_ region merging
10. How is line detected? Explain through the operators
_ Types of line masks
1. horizontal
2. vertical
3. +45°,-45°
IN previous posts ,we added Two Marks for All 5 Units,here in this post we have added 16 marks questions and answers type for Digital Image Processing (DIP).
If you lik this posts/our works,It will be helpful if you share this to your frieds @facebook,@Twitter.
You may need to look :
* Two Marks Question and Answers :
- Unit 1
- Unit 2
- Unit 3
- Unit 4
- Unit 5
Have any queries ? ask it via comments.
16 MARKS for Digital Image Processing EC2029
UNIT I
1. Explain the steps involved in digital image processing.
(or)
Explain various functional block of digital image processing
# Image acquisition
# Preprocessing
# Segmentation
# Representation and Description
# Recognition and Interpretation
2. Describe the elements of visual perception.
# Cornea and Sclera
# Choroid – Iris diaphragm and Ciliary body
# Retina- Cones and Rods
3. Describe image formation in the eye with brightness adaptation and
discrimination
# Brightness adaptation
# Subjective brightness
# Weber ratio
#Mach band effect
#simultaneous contrast
4. Write short notes on sampling and quantization.
# Sampling
# Quantization
# Representing Digital Images
5. Describe the functions of elements of digital image processing system
with a diagram.
# Acquisition
# Storage
# Processing
# Communication
# Display
6. Explain the basic relationships between pixels?
# Neighbors of a pixel
# Connectivity, Adjacency, Path
# Distance Measure
# Arithmetic and Logic Operations
7. Explain the properties of 2D Fourier Transform.
# Separability
# Translation
# Periodicity and Conjugate Symmetry
# Rotation
# Distribution and Scaling
# Average Value
# Laplacian
# Convolution and correlation
# Sampling
8. ( i )Explain convolution property in 2D fourier transform.
* 1D Continuous
* 1D Discrete
* 1D convolution theorem
* 2D continuous
* 2D Discrete
* 2D convolution theorem
(ii) Find F (u) and |F (u)|
9. Explain Fast Fourier Transform (FFT) in detail.
# FFT Algorithm
# FFT Implementation
10. Explain in detail the different separable transforms
# Forward 1D DFT & 2D DFT
# Inverse 1D DFT & 2D DFT
# Properties
11. Explain Hadamard transformation in detail.
# 1D DHT
# 1D Inverse DHT
# 2D DHT
# 2D Inverse DHT
12. Discuss the properties and applications of
1)Hadamard transform 2)Hotelling transform
# Properties of hadamard:
Real and orthogonal
fast transform
faster than sine transform
Good energy compaction for image
# Appl:
Image data compression,
filtering and design of course
# Properties of hotelling:
Real and orthogonal
Not a fast transform
Best energy compaction for image
# Appl:
Useful in performance evaluation & for finding performance
Bounds
13. Explain Haar transform in detail.
# Def P= 2P+q-1
# Find h k (z)
14. Explain K-L transform in detail.
Consider a set of n or multi-dimensional discrete signal represented as column
vector x1,x2,…xn each having M elements,
X1
X2
X= .
.
Xn
The mean vector is defined as Mx=E{x}
Where E{x} is the expected value of x. M
For M vector samples mean vector is Mx=1/M _ Xk
K=1
T
The co-variant matrix is, Cx=E{(X-Mx)(X-Mx)}
M T
For M samples, Cx=1/M _ (xk-Mx)(xk-Mx).
K=1
K-L Transform Y= A (X- MX)
UNIT II
1. Explain the types of gray level transformation used for image enhancement.
# Linear (Negative and Identity)
# Logarithmic( Log and Inverse Log)
# Power_law (nth root and nth power)
# Piecewise_linear (Constrast Stretching, Gray level Slicing,
Bit plane Slicing)
2. What is histogram? Explain histogram equalization.
# P(rk) = nk/n
# Ps(s) = 1 means histogram is arranged uniformly.
3. Discuss the image smoothing filter with its model in the spatial domain.
# LPF-blurring
# Median filter – noise reduction & for sharpening image
4. What are image sharpening filters. Explain the various types of it.
# used for highlighting fine details
# HPF-output gets sharpen and background becomes darker
# High boost- output gets sharpen but background remains unchanged
# Derivative- First and Second order derivatives
Appl:
# Medical image
# electronic printing
# industrial inspection
5. Explain spatial filtering in image enhancement.
# Basics
# Smoothing filters
# Sharpening filters
6. Explain image enhancement in the frequency domain.
# Smoothing filters
# Sharpening filters
# Homomorphic filtering
7. Explain Homomorphic filtering in detail.
# f(x, y) = i(x, y) . r(x, y)
# Calculate the enhanced image g(x,y)
UNIT III
1. Explain the algebra approach in image restoration.
# Unconstrained
# Constrained
2. What is the use of wiener filter in image restoration. Explain.
# Calculate f^
# Calculate F^(u, v)
3. What is meant by Inverse filtering? Explain.
# Recovering i/p from its o/p
# Calculate f^(x, y)
4. Explain singular value decomposition and specify its properties.
# U= m=1_r___m _m
T
This equation is called as singular value decomposition of an image.
# Properties
The SVD transform varies drastically from image to image.
The SVD transform gives best energy packing efficiency for any given
image.
The SVD transform is useful in the design of filters finding least
square,minimum solution of linear equation and finding rank of large
matrices.
5. Explain image degradation model /restoration process in detail.
# Image degradation model /restoration process diagram
# Degradation model for Continuous function
# Degradation model for Discrete function – 1_D and 2_D
6. What are the two approaches for blind image restoration? Explain in detail.
_ Direct measurement
_ Indirect estimation
UNIT IV
1. What is data redundancy? Explain three basic data redundancy?
Definition of data redundancy
The 3 basic data redundancy are
_ Coding redundancy
_ Interpixel redundancy
_ Psycho visual redundancy
2. What is image compression? Explain any four variable length coding
compression schemes.
Definition of image compression
Variable Length Coding
* Huffman coding
* B2 Code
* Huffman shift
* Huffman Truncated
* Binary Shift
*Arithmetic coding
3. Explain about Image compression model?
The source Encoder and Decoder
The channel Encoder and Decoder
4. Explain about Error free Compression?
a. Variable Length coding
i. Huffman coding
ii. Arithmetic coding
b. LZW coding
c. Bit Plane coding
d. Lossless Predictive coding
5. Explain about Lossy compression?
Lossy predictive coding
Transform coding
Wavelet coding
6. Explain the schematics of image compression standard JPEG.
Lossy baseline coding system
Extended coding system
Lossless Independent coding system
7. Explain how compression is achieved in transform coding and explain about DCT
_ Block diagram of encoder
_ decoder
_ Bit allocation
_ 1D transform coding
_ 2D transform coding, application
_ 1D,2D DCT
8. Explain arithmetic coding
_ Non-block code
_ One example
9. Explain about Image compression standards?
_ Binary Image compression standards
_ Continuous tone still Image compression standards
_ Video compression standards
10. Discuss about MPEG standard and compare with JPEG
_ Motion Picture Experts Group
1. MPEG-1
2. MPEG-2
3. MPEG-4
_ Block diagram
_ I-frame
_ p-frame
_ B-frame
UNIT V
1. What is image segmentation. Explain in detail.
Definition - image segmentation
Discontinity – Point, Line, Edge
Similarity – Thresholding, Region Growing, Splitting and
Merging
2. Explain Edge Detection in details?
* Basic formation.
* Gradient Operators
* Laplacian Operators
3. Define Thresholding and explain the various methods of thresholding in detail?
Foundation
The role of illumination
Basic adaptive thresholding
Basic adaptive thresholding
Optimal global & adaptive thresholding.
4. Discuss about region based image segmentation techniques. Compare
threshold region based techniques.
* Region Growing
* Region splitting and merging
* Comparison
5. Define and explain the various representation approaches?
chain codes
Polygon approximations
Signature
Boundary segments
Skeletons.
6. Explain Boundary descriptors.
Simple descriptors.
Fourier descriptors.
7. Explain regional descriptors
Simple descriptors
Texture
i. Statistical approach
ii. Structural approach
iii. Spectral approach
8. Explain the two techniques of region representation.
_ Chain codes
_ Polygonol approximation
9. Explain the segmentation techniques that are based on finding the regions
directly.
_ Edge detection line detection
_ Region growing
_ Region splitting
_ region merging
10. How is line detected? Explain through the operators
_ Types of line masks
1. horizontal
2. vertical
3. +45°,-45°
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