Intensity Transformation MCQ [Free PDF] – Objective Question Answer for Intensity Transformation Quiz

1. Noise reduction is obtained by blurring the image using a smoothing filter.

A. True
B. False

Answer: A

Noise reduction is obtained by blurring the image using a smoothing filter. Blurring is used in pre-processing steps, such as the removal of small details from an image prior to object extraction and, bridging of small gaps in lines or curves.

 

2. What is the output of a smoothing, linear spatial filter?

A. Median of pixels
B. Maximum of pixels
C. Minimum of pixels
D. Average of pixels

Answer: D

The output or response of smoothing, the linear spatial filter is simply the average of the pixels contained in the neighborhood of the filter mask.

 

3. Smoothing linear filter is also known as a median filter.

A. True
B. False

Answer: B

Since the smoothing spatial filter performs the average of the pixels, it is also called an averaging filter.

 

4. Which of the following in an image can be removed by using a smoothing filter?

A. Smooth transitions of gray levels
B. Smooth transitions of brightness levels
C. Sharp transitions of gray levels
D. Sharp transitions of brightness levels

Answer: C

The smoothing filter replaces the value of every pixel in an image with the average value of the gray levels. So, this helps in removing the sharp transitions in the gray levels between the pixels. This is done because random noise typically consists of sharp transitions in gray levels.

 

5. Which of the following is the disadvantage of using a smoothing filter?

A. Blur edges
B. Blur inner pixels
C. Remove sharp transitions
D. Sharp edges

Answer: A

Edges, which almost always are desirable features of an image, also are characterized by sharp transitions in gray levels. So, averaging filters have an undesirable side effect that they blur these edges.

 

6. Smoothing spatial filters doesn’t smooth the false contours.

A. True
B. False

Answer: B

One of the applications of smoothing spatial filters is that they help in smoothing the false contours that result from using an insufficient number of gray levels.

 

7. The mask shown in the figure below belongs to which type of filter?

A. Sharpening spatial filter
B. Median filter
C. Sharpening frequency filter
D. Smoothing spatial filter

Answer: D

This is a smoothing spatial filter. This mask yields a so-called weighted average, which means that different pixels are multiplied with different coefficient values. This helps in giving much importance to some pixels at the expense of others.

 

8. The mask shown in the figure below belongs to which type of filter?

A. Sharpening spatial filter
B. Median filter
C. Smoothing spatial filter
D. Sharpening frequency filter

Answer: C

The mask shown in the figure represents a 3×3 smoothing filter. The use of this filter yields the standard average of the pixels under the mask.

 

9. Box filter is a type of smoothing filter.

A. True
B. False

Answer: A

A spatial averaging filter or spatial smoothening filter in which all the coefficients are equal is also called a box filter.

 

10. If the size of the averaging filter used to smooth the original image to the first image is 9, then what would be the size of the averaging filter used in smoothing the same original picture to the second in the second image?

A. 3
B. 5
C. 9
D. 15

Answer: D

We know that as the size of the filter used in smoothening the original image that is averaging filter increases then the blurring of the image. Since the second image is more blurred than the first image, the window size should be more than 9.

 

11. Which of the following comes under the application of image blurring?

A. Object detection
B. Gross representation
C. Object motion
D. Image segmentation

Answer: B

An important application of spatial averaging is to blur an image to get a gross representation of interesting objects, such that the intensity of the small objects blends with the background and large objects become easy to detect.

 

12. Which of the following filter’s responses is based on the ranking of pixels?

A. Nonlinear smoothing filters
B. Linear smoothing filters
C. Sharpening filters
D. Geometric mean filter

Answer: A

Order static filters are nonlinear smoothing spatial filters whose response is based on the ordering or ranking of the pixels contained in the image area encompassed by the filter, and then replace the value of the central pixel with the value determined by the ranking result.

 

13. Median filter belongs to which category of filters?

A. Linear spatial filter
B. Frequency domain filter
C. Order static filter
D. Sharpening filter

Answer: C

The median filter belongs to order static filters, which, as the name implies, replaces the value of the pixel by the median of the grey levels that are present in the neighborhood of the pixels.

 

14. Median filters are effective in the presence of impulse noise.

A. True
B. False

Answer: A

Median filters are used to remove impulse noises, also called salt-and-pepper noise because of their appearance as white and black dots in the image.

 

15. What is the maximum area of the cluster that can be eliminated by using an n×n median filter?

A. n2
B. n2/2
C. 2*n2
D. n

Answer: B
Isolated clusters of pixels that are light or dark with respect to their neighbors, and whose area is less than n2/2, i.e., half the area of the filter, can be eliminated by using an n×n median filter.

 

16. Which of the following expression is used to denote spatial domain process?

A. g(x,y)=T[f(x,y)]
B. f(x+y)=T[g(x+y)]
C. g(xy)=T[f(xy)]
D. g(x-y)=T[f(x-y)]

Answer: A

Spatial domain processes will be denoted by the expression g(x,y)=T[f(x,y)], where f(x,y) is the input image, g(x,y) is the processed image, and T is an operator on f, defined over some neighborhood of (x, y). In addition, T can operate on a set of input images, such as performing the pixel-by-pixel sum of K images for noise reduction.

 

17. Which of the following shows three basic types of functions used frequently for image enhancement?

A. Linear, logarithmic, and inverse law
B. Power-law, logarithmic, and inverse law
C. Linear, logarithmic and power-law
D. Linear, exponential, and inverse law

Answer: B

The introduction to gray-level transformations shows three basic types of functions used frequently for image enhancement: linear (negative and identity transformations), logarithmic (log and inverse-log transformations), and power-law (nth power and nth root transformations). The identity function is the trivial case in which output intensities are identical to input intensities. It is included in the graph only for completeness.

 

18. Which expression is obtained by performing the negative transformation on the negative of an image with gray levels in the range[0, L-1]?

A. s=L+1-r
B. s=L+1+r
C. s=L-1-r
D. s=L-1+r

Answer: C

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.

 

19. What is the general form of representation of log transformation?

A. s=clog10(1/r)
B. s=clog10(1+r)
C. s=clog10(1*r)
D. s=clog10(1-r)

Answer: B

The general form of the log transformation: s=clog10(1+r), where c is a constant, and it is assumed that r ≥ 0.

 

20. What is the general form of representation of power transformation?

A. s=crγ
B. c=srγ
C. s=rc
D. s=rcγ

Answer: A

Power-law transformations have the basic form: s=crγ where c and g are positive constants. Sometimes s=crγ is written as s=c.(r+ε)γ to account for an offset (that is, a measurable output when the input is zero).

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