Aloysius Institute of Management and Information Technology (AIMIT)

St. Aloysius College (Autonomous)

Paper: PS 604.2 [E2]: IMAGE PROCESSING AND PATTERN RECOGNITION

QUESTION BANK
UNIT I
1. What is meant by Digital Image Processing? Explain how digital images can 10
be represented?
2. Discuss the different components of Digital Image Processing with a 10
diagram.
3. Illustrate the fundamental steps in Digital Image Processing with a diagram. 10
4. “In the field of Digital Image Processing human intuition and analysis play a 10
central role in the choice of one technique versus another”. Justify this
statement.
5. Explain the process of image acquisition. Illustrate with example. 10
6. Explain the concepts of image sampling and quantization. 10
7. Write a short note on Image interpolation. 5
8. Discuss some of the basic relationships between pixels in a digital image. 10
9. Choose some examples of fields that use Digital Image Processing. 10
10. Write short notes on the following: 10
a. X-Ray Imaging
b. Gamm Ray Imaging
11. Write short notes on the following: 10
a. Imaging in the microwave band
b. Imaging in the Ultraviolet band
12. Write short notes on the following: 20
a. Adjacency
b. Connectivity
c. Regions
d. Boundaries
13. Discuss some of the distance measures used between pixels in a digital 10
image.
14. Describe the concept of linearity and nonlinearity in an image processing 10
method with examples.
15. Justify the following statement: 10
a. H, a sum operator is linear.
b. H, a max operator which is a function to find the maximum value of
the pixels in an image is nonlinear
UNIT II
1. Explain the types of gray level transformation used for image enhancement. 10
2. “Histogram are the basis for numerous spatial domain processing 10
techniques.” Justify the statement.
3. Briefly discuss the different types of processing in an Histogram. 10
4. Discuss the concept of Correlation and Convolution in linear spatial filtering 10
with an example.
5. Discuss the image smoothing filter with its model in the spatial domain. Also 10
explain its significance in digital image processing.
6. Explain the basic steps of filtering in frequency domain with a diagram. 10
7. Discuss the various image sharpening filters present in frequency domain. 10
8. Discuss the various image smoothing filters present in frequency domain. 10
9. Explain the usage of Homomorphic filtering in frequency domain. 10
10. Explain image degradation /restoration model with diagram. 6
11. Discuss the various noise probability density functions found in image 10
processing applications.
12. Briefly discuss the noise reduction capabilities of the following spatial 20
filters:
a. Arithmetic mean filter
b. Geometric mean filter
c. Harmonic mean filter
d. Contraharmonic mean filter
13. “Periodic noise can be analyzed and filtered effectively using frequency 10
domain techniques”. Justify the statement.
14. Write short notes on: 10
a. Minimum mean square error filter
b. Constrained least square filter
15. Explain homogeneity property in Linear Operator. What are the advantages 10
and what are the limitations.
16. Write short notes on: 10
a. Weiner filter
b. Geometric mean filtering
c. Inverse filtering

UNIT III
1. Describe the two categories of Image segmentation. 10
2. Briefly explain the different approaches for image segmentation. 10
3. Illustrate the segmentation methods based on points, lines and edges. 10
4. Discuss the different edge models classified according to their intensity 10
profiles.
5. Describe any one algorithm used for edge detection. 10
6. Define thresholding and explain the various techniques of thresholding in 10
detail.
7. Discuss the different region based image segmentation techniques. 10
8. What is meant by edge linking? Explain edge linking using local processing 10
9. Define image feature? How are the features classified? 10
10. Discuss the classification of feature extraction in Image processing. 10
11. Define Representation. Explain two types of Image representation with an
example.
12. Explain Chain Code with an example. 8
 13. Discuss Fourier Descriptors of the boundary. What are the properties of 8
Fourier Descriptors?
14. Write short notes on Image representation: 20
a. Using boundary descriptors
b. Using regional descriptors

UNIT IV
1. Discuss the different elements of Image Analysis. 10
2. Interpret the different components of Pattern recognition system with an 10
example.
3. Explain the design cycle of a Pattern recognition system. 10
4. Discuss the different forms of learning with examples. 20
5. Distinguish between the Feature selection and Feature extraction with an 10
example.
6. What are feature vectors? Explain the concept of dimensionality reduction 10
with respect to feature vectors.
7. Explain the concept of feature space with respect to machine learning. 10
8. Discuss the concept of clustering with an example. 10
9. Illustrate the different distance measures used in Clustering. 10
10. Briefly explain supervised and unsupervised parameter estimation with an 10
example.
11. Briefly explain the different clustering algorithms available. 10
12. Illustrate hierarchical clustering algorithm with an example. 10
13. Demonstrate k-means algorithm with an example. 10

UNIT V
1. Explain the two-class problem in Bayes decision theory. 10
2. Discuss the pattern classification carried out using a statistical classifier. 10
3. Describe the different performance evaluation measures used for measuring a 10
classifier.
4. Explain the two different paradigms of Pattern recognition. 10
5. Write short notes on the following: 10
a. Syntactic pattern recognition
b. Statistical pattern recognition
6. Write short notes on the following: 10
a. Syntactic pattern recognition
b. Fuzzy pattern recognition
7. Briefly explain some applications of Pattern recognition. 10
89. Discuss Fuzzy classification with an example. 10
9. Discuss the concept of Fuzzy clustering. Illustrate the same with an example. 10
10. Discuss how pattern recognition is applied in various research areas. 10
11. Describe any one application of Pattern recognition in detail. 10
 Aloysius Institute of Management and Information Technology (AIMIT)
St. Aloysius College (Autonomous)
Mangalore

Model question paper

Paper: PS 605.2 [E2]: IMAGE PROCESSING AND PATTERN RECOGNITION

Answer FIVE full questions; ONE Full Question from every Section
Section I

1. a) Explain about image sampling and quantization process. 10

b) What are the fundamental steps in Digital Image Processing? 10

2. a) Explain the process of image acquisition. Illustrate with example. 10

Section II

3. a) Explain the types of gray level transformation used for image enhancement. 10

b) Discuss the various image smoothing filters present in frequency domain. 10

4. a) What is the use of wiener filter and least means square filter in image restoration?
Explain. 10
b) Discuss the various noise probability density functions found in image processing 10
applications.

Section III

5. a) Discuss the different edge models classified according to their intensity profiles. 10
b) Define thresholding and explain the various techniques of thresholding in detail. 10

6. a) Write short notes on Image representation: 20

Section IV

7. a) Interpret the different components of Pattern recognition system with an example. 10

b) What are feature vectors? Explain the concept of dimensionality reduction with respect 10
to feature vectors.
8. a) Demonstrate k-means algorithm with an example. 10
b) Distinguish between the Feature selection and Feature extraction with an example. 10
Section V

9. a) Describe the different performance evaluation measures used for measuring a classifier. 10

b) Write short notes on the following: 10

10. a) Discuss the pattern classification carried out using a statistical classifier. 10

b) Explain the two different paradigms of Pattern recognition. 10