Welcome to the Intensity Transformations and Spatial Filtering MCQs Page

Dive deep into the fascinating world of Intensity Transformations and Spatial Filtering with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Intensity Transformations and Spatial Filtering, a crucial aspect of Digital Image Processing (DIP). In this section, you will encounter a diverse range of MCQs that cover various aspects of Intensity Transformations and Spatial Filtering, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Digital Image Processing (DIP).

Check out the MCQs below to embark on an enriching journey through Intensity Transformations and Spatial Filtering. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Digital Image Processing (DIP).

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Intensity Transformations and Spatial Filtering. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Intensity Transformations and Spatial Filtering MCQs | Page 14 of 27

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Q131.

How is the expression represented for the normalized histogram?

p(r_k )=n_k

p(r_k )=n_k/n

p(r_k)=nn_k

p(r_k )=n/n_k

Discuss

Answer: (b).p(r_k )=n_k/n

Q132.

Which of the following conditions does the T(r) must satisfy?

T(r) is double-valued and monotonically decreasing in the interval 0≤r≤1; and

T(r) is double-valued and monotonically increasing in the interval 0≤r≤1; and

T(r) is single-valued and monotonically decreasing in the interval 0≤r≤1; and

T(r) is single-valued and monotonically increasing in the interval 0≤r≤1; and

Discuss

Answer: (d).T(r) is single-valued and monotonically increasing in the interval 0≤r≤1; and

Q133.

The inverse transformation from s back to r is denoted as:

s=T^-1(r) for 0≤s≤1

r=T^-1(s) for 0≤r≤1

r=T^-1(s) for 0≤s≤1

r=T^-1(s) for 0≥s≥1

Discuss

Answer: (c).r=T^-1(s) for 0≤s≤1

Q134.

The probability density function p_s (s) of the transformed variable s can be obtained by using which of the following formula?

p_s (s)=p_r (r)|dr/ds|

p_s (s)=p_r (r)|ds/dr|

p_r (r)=p_s (s)|dr/ds|

p_s (s)=p_r (r)|dr/dr|

Discuss

Answer: (a).p_s (s)=p_r (r)|dr/ds|

Q135.

A transformation function of particular importance in image processing is represented in which of the following form?

s=T(r)=∫0^(2r)pr (ω)dω

s=T(r)=∫0^(r-1)pr (ω)dω

s=T(r)=∫0^(r/2)pr (ω)dω

s=T(r)=∫0 pr (ω)dω

Discuss

Answer: (d).s=T(r)=∫0 pr (ω)dω

Q136.

Histogram equalization or Histogram linearization is represented by of the following equation:

sk =∑k j =1 nj/n k=0,1,2,……,L-1

sk =∑k j =0 nj/n k=0,1,2,……,L-1

sk =∑k j =0 n/nj k=0,1,2,……,L-1

sk =∑k j =n nj/n k=0,1,2,……,L-1

Discuss

Answer: (b).sk =∑k j =0 nj/n k=0,1,2,……,L-1

Q137.

What is the method that is used to generate a processed image that have a specified histogram?

Histogram linearization

Histogram equalization

Histogram matching

Histogram processing

Discuss

Answer: (c).Histogram matching

Q138.

Histograms are the basis for numerous spatial domain processing techniques.

True

False

May be

Can't Say

Discuss

Answer: (a).True

Q139.

In a dark image, the components of histogram are concentrated on which side of the grey scale?

High

Medium

Low

Evenly distributed

Discuss

Answer: (c).Low

Q140.

What is the basis for numerous spatial domain processing techniques?

Transformations

Scaling

Histogram

None of the Mentioned

Discuss

Answer: (c).Histogram

Page 14 of 27

Welcome to the Intensity Transformations and Spatial Filtering MCQs Page

Intensity Transformations and Spatial Filtering MCQs | Page 14 of 27

Explore more Topics under Digital Image Processing (DIP)

Basics of Digital Image Processing

Digital Image Fundamentals

Filtering in Frequency Domain

Image Compression

Image Restoration and Reconstruction

Color Image Processing

Image Segmentation

Morphological Image Processing

Wavelet and Multiresolution Processing

Image Enhancement

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