Selected_ quiz2
1. In.……...at each location, the histogram of the points in the
neighborhood is computed. Either histogram equalization or histogram
specification transformation function is obtained.
a) Local Histogram processing b) Global Histogram processing
c) Universal Histogram processing
2. ……..expands the range of intensity levels in an image so that it spans
the full intensity range of the recording medium or display device.
a) Contrast stretching b) Bit-plane Slicing
c) Intensity-level Slicing
3. ………..Highlighting a specific range of intensities in an image often
is of interest.
a) Contrast Stretching b) Bit-plane Slicing
c) Intensity-level Slicing
4. Contrast Stretching is a way to
a) Expands the range of intensity levels in an image so that it spans
the full intensity range of the recording medium or display device.
b) Highlighting a specific range of intensities in an image often is
of interest.
c) transform coefficient
5. Intensity-level Slicing is
a) Process transformation, not directly process the intensity values of
the image plane.
b) Highlighting a specific range of intensities in an image often is of
interest.
c) Expands the range of intensity levels in an image so that it spans the
full intensity range of the recording medium or display device.
6. …………is an image plane itself, directly process the intensity values
of the image plane.
a) Subdomain
b) Spatial domain
c) Transform domain
7. ……………is the process that transforms coefficients, not directly
process the intensity values of the image plane.
a) Subdomain
b) Spatial domain
c) Transform domain
8. …………refers to passing, modifying, or rejecting specified
frequency components of an image.
a) Blurring
b) Filtering
c) Sampling
9. …………. modifies an image by replacing the value of each pixel
with a function of the values of the pixel and its neighbors.
a) Spatial filtering b) Blurring
c) Histogram
10. The best-known filter in this category is……..
a) linear filtering
b) non-linear filtering
c) median filter.
11. ……….provide excellent noise reduction capabilities for certain
types of random noise.
a) Linear filtering b) non-linear filtering c) median filter
12. ……….is particularly effective in the presence of impulse noise.
a) Linear filtering b) non-linear filtering c) median filter
13. The median represents the ………percentile of a ranked set of
numbers but ranking lends itself to many other possibilities.
a)50th
b) 60th
c) 70th
14. Foundation, Laplacian Operator, Unsharp Masking, and Highboost
Filtering are examples of……………
a) Smoothing spatial filtering b) sharpening Spatial Filters
c) silky spatial filtering.
15. Sharpening Spatial Filters consist of
a) Unsharp Masking and Highboost Filtering
b) Using First-Order Derivatives for Nonlinear Image Sharpening
the Gradient
c) ALL the above
16. A spatial filter consists of
a) neighborhood b) a predefined operation c) Both a&b
17. Smoothing filters are used for
a) Blurring
b) noise reduction
c) Both a&b
18. Blurring is used in
a) Removal of small details b) bridging of small gaps in lines or
curves
c) Both a& b
19. Smoothing spatial filters include
a) linear filters
b) nonlinear filters
c) Both a&b
20. Blurring and noise reduction are uses of …………...
a) Smoothing filters b) Blurring
c) Histogram
21. Removal of small details and bridging of small gaps in lines or
curves are uses of………
a) Smoothing filters b) Blurring
c) Histogram
22. ………performs a sum-of-products operation between an image f
and a filter kernel, w.
a) Linear spatial filtering b) non-linear spatial filtering c) both a& b
23. ……….is an array whose size defines the neighborhood of
operation, and whose coefficients determine the nature of the filter.
a) Sigma
b) Alpha
c) Kernel
24. ……………refers to pixel regions that are small with respect to the
size of the filter kernel.
a) Relevant
b) Irrelevant
c) Insufficient
25. …………. based on ordering (ranking) the pixels contained in the
filter mask.
a) Linear
b) non-linear
c) both a& b
26. Median filter, max filter, and min filter are examples of …….
a) linear
b) non-linear
c) both a& b.
27. ………. consists of moving the center of a kernel over an image
and computing the sum of products at each location.
a) Correlation
b) Convolution kernel c) Both of them
28. To denote filter kernels of the type we have been discussing we use
……...
a) Correlation
b) Convolution kernel c) Both of them.
29. ………. which is the basis for filtering in the spatial domain is
equivalent to multiplication in the frequency domain, and vice versa.
a) Correlation
b) Convolution kernel
c) Both of them
30. The mechanics of spatial convolution are the same, except that the
correlation kernel is rotated by………….
a) 45°
b) 90°
c) 180°
31. A filter that passes low frequencies is called a
…………….
a) highpass filter b) lowpass filter
c)Both a& b
32. …….. are suitable for quick experimentation and they often yield
smoothing results that are visually acceptable.
a) Box filters
b) highpass
c)bandreject
33. ……….. consists of subtracting an unsharp (smoothed) version of
an image from the original image.
a) Unsharp masking
b) Highboost Filtering
c) none of them
34. 1. Blur the original image.
2. Subtract the blurred image from the original (the resulting
difference is called the mask.)
3. Add the mask to the original unsharp masking,
Are steps of …...
a) Unsharp masking
b) Highboost Filtering c)none of them.
35. Printing and publishing industries are examples ……
a) Unsharp masking
b) Highboost Filtering c) none of them
36.
𝜕𝑓
𝜕𝑥
= 𝑓 (𝑥 + 1) − 𝑓 (𝑥 ) this formula expresses…… of a one-
dimensional function 𝑓𝑥() as the difference.
a) first-order derivative
b) second-order derivative
order derivative.
37.
𝜕2𝑓
𝜕2𝑥
c) third-
= 𝑓 (𝑥 + 1) + 𝑓(𝑥 − 1) − 2𝑓(𝑥) this formula expresses…..
a) first-order derivative
b) second-order derivative
c) thirdorder derivative.
38. Must be zero in areas of constant intensity.
2. Must be nonzero at the onset of an intensity step or ramp.
3. Must be nonzero along intensity ramps.
Are steps of …….
a) first-order derivative
b) second-order derivative
c) Both
39. The filter type lowpass expressed in terms of lowpass filters as
a) 𝑙𝑝(𝑥, 𝑦)
a) ℎ𝑝(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦) − 𝑙𝑝(𝑥, 𝑦)
b) 𝑏𝑝(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦) − 𝑏𝑟(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦)[𝑙𝑝1(𝑥, 𝑦) +
[𝛿 (𝑥, 𝑦) − 𝑙𝑝2(𝑥, 𝑦)]]
40. This …….. property is quite useful for locating edges.
a) a) zero crossing
b) first crossing
c) second crossing
41. ………… in digital images often are ramp-like transitions in
intensity.
a) Sides
b) Edges
c) Dimensions
42. The……… of the image would result in thick edges because the
derivative is nonzero along a ramp.
a) first derivative
b) second derivative
c) third derivative
43. The ……… would produce a double edge one pixel thick,
separated by zeros.
a) first derivative, b) second derivative
c) third derivative
44. The filter type highpass is expressed in terms as
a)𝑙𝑝(𝑥, 𝑦)
b) ℎ𝑝(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦) − 𝑙𝑝(𝑥, 𝑦)
c)𝑏𝑝(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦) − 𝑏𝑟 (𝑥, 𝑦) = 𝛿 (𝑥, 𝑦)[𝑙𝑝1(𝑥, 𝑦) +
[𝛿 (𝑥, 𝑦) − 𝑙𝑝2(𝑥, 𝑦)]]
45. The filter type bandpass expressed in terms as
a) 𝑙𝑝(𝑥, 𝑦)
b) ℎ𝑝(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦) − 𝑙𝑝(𝑥, 𝑦)
c) 𝑏𝑝(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦) − 𝑏𝑟 (𝑥, 𝑦) = 𝛿 (𝑥, 𝑦)[𝑙𝑝1(𝑥, 𝑦) +
[𝛿 (𝑥, 𝑦) − 𝑙𝑝2(𝑥, 𝑦)]]
46. The filter type bandreject is expressed in terms as
a) ℎ𝑝(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦) − 𝑙𝑝(𝑥, 𝑦)
b) 𝑏𝑝(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦) − 𝑏𝑟(𝑥, 𝑦) = 𝛿 (𝑥, 𝑦)[𝑙𝑝1(𝑥, 𝑦) +
[𝛿 (𝑥, 𝑦) − 𝑙𝑝2(𝑥, 𝑦)]]
c) None of the above
47. smoothed using ….………kernel with the sizes and parameters.
a) box
b) Gaussian
c)Both a& b
48. The……. filter yielded significantly smoother results around the edge
transitions.
a) box
b) Gaussian
c)Both a& b
49. ……....., in which values outside the boundary of the image are obtained by
mirror-reflecting the image across its border.
a) mirror padding
b) replicate padding
c) Both a& b
50. ……….., in which values outside the boundary are set equal to the nearest
image border value.
a) mirror padding
b) replicate padding
c) Both a& b
51.
mirror padding is more applicable when the areas near the border contain
image details. In other words, these two types of padding attempt to ……...
characteristics of an image past its borders.
a) intend
b) extend