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Programming > CSCE4240 - Exam 2 > Flashcards

CSCE4240 - Exam 2 Flashcards

1
Q

histogram algorithm

step 1.

A

create a one-dimensional array h of size L with initial value of zero

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2
Q

histogram algorithm
step 1. create one-dimensional array h of size L with initial value of zero
step 2.

A

loop through all pixels in the image A

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3
Q

histogram algorithm
step 1. create one-dimensional array h of size L with initial value of zero
step 2. loop through all pixels in the image
step 3.

A

for a pixel value v, increase the value at h(v) by one

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4
Q

histogram algorithm
step 1. create one-dimensional array h of size L with initial value of zero
step 2. loop through all pixels in the image
step 3. for a pixel value v, increase the value of h(v) by one
step 4.

A

continue until all pixels in the image A are visited

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5
Q

what is an image histogram

A

acts as a graphical representation of the tonal distribution in a digital image

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6
Q

what is histogram stretching

A

maps the value of all pixels to a new value that spans the full gray scale range

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7
Q

what is histogram equalization

A

maximize the usage of the full brightness range. maximum contrast is achieved when image histogram is uniform distribution

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8
Q

median filter algorithm

A

sort all the pixels in an increasing order and take the middle value

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9
Q

what are sampling rates

A

rate at which amplitude values are digitized from the original waveform

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10
Q

what is sinusoidal basis

A

use smoothly-varying sinusoidal patters at different frequencies, angles for basis of images - hadamard basis doesn’t capture real image gradients

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11
Q

what is magnitude

A

how much of a certain frequency component is in an image

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12
Q

what is phase

A

where that certain frequency lies

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13
Q

butterworts lowpass filter

A

introduces unwanted artifacts into the result. uses smooth transition

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14
Q

JPEG encoding

step 1.

A

transform RGB to YUV and subsample color

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15
Q

JPEG encoding
step 1. transform RGB to YUV and subsample color
step 2.

A

perform discrete cosine transform on 8x8 image blocks

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16
Q

JPEG encoding
step 1. transform RGB to YUV and subsample color
step 2. perform discrete cosine transform on 8x8 image blocks
step 3.

A

perform quantization

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17
Q

JPEG encoding
step 1. transform RGB to YUV and subsample color
step 2. perform discrete cosine transform on 8x8 image blocks
step 3. perform quantization
step 4.

A

zig-zag ordering and run-length encoding

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18
Q

JPEG encoding
step 1. transform RGB to YUV and subsample color
step 2. perform discrete cosine transform on 8x8 image blocks
step 3. perform quantization
step 4. zig-zag ordering and run-length encoding
step 5.

A

entropy encoding

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19
Q

what is quantization

A

aims at reducing the total number of bits by dividing each entry in the frequency space block by an integer then round

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20
Q

wavelet-based image fusion

step 1.

A

decompose images using wavelet transform

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21
Q

wavelet-based image fusion
step 1. decompose images using wavelet transform
step 2.

A

combine coefficients

  1. combine approximation subbing and the average
  2. select the maximum among detail subtends and put in the composite
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22
Q

wavelet-based image fusion
step 1. decompose images using wavelet transform
step 2. combine coefficients
step 3.

A

perform inverse wavelet transform on the composite wavelet matrix

23
Q

noise-aware image fusion algorithm

step 1.

A

decompose images with wavelet transform

24
Q

noise-aware image fusion algorithm
step 1. decompose images with wavelet transform
step 2.

A

compute the subbing noise variance On

25
Q

noise-aware image fusion algorithm
step 1. decompose images with wavelet transform
step 2. compute the subbing noise variance
step 3.

A

estimate the threshold (lamda) that separate SIC and NIC

26
Q

noise-aware image fusion algorithm
step 1. decompose images with wavelet transform
step 2. compute the subbing noise variance
step 3. estimate the threshold (lamda) that separate SIC and NIC
step 4/5.

A
  1. combine SIC

5. combine NIC

27
Q

noise-aware image fusion algorithm
step 1. decompose images with wavelet transform
step 2. compute the subbing noise variance
step 3. estimate the threshold (lamda) that separate SIC and NIC
step 4/5. combine SIC and combine NIC
step 6.

A

synthesize a fused image from the subbing composite

28
Q

JPEG2000 algorithm

1.

A

image tilting

29
Q

JPEG2000 algorithm
1. image tilting
2.

A

DC-level shifting

30
Q

JPEG2000 algorithm
1. image tilting
2. DC-level shifting
3.

A

components tranformation

31
Q 
JPEG2000 algorithm
1. image tilting
2. DC-level shifting
3. components tranformation
4.
A

wavelet transform

32
Q 
JPEG2000 algorithm
1. image tilting
2. DC-level shifting
3. components tranformation
4. wavelet transform
5.
A

quantization

33
Q 
JPEG2000 algorithm
1. image tilting
2. DC-level shifting
3. components tranformation
4. wavelet transform
5. quantization
6.
A

coefficient coding

34
Q

Image morphology edge detection

1.

A

dilate the original image

35
Q

Image morphology edge detection
1. dilate the original image
2.

A

subtract the original image from the dilated one

36
Q

looks for particular pattern within the image

A

hit-and-miss transform

37
Q

delete any such point that has more than one foreground neighbor, as long as doing so does not locally disconnect the region

A

thinning

38
Q

suppresses the bright details that are smaller than the specified SE

A

opening

39
Q

suppresses the dark details

A

closing

40
Q

Chain Code

1.

A

find the top-left pixel on the boundary; call P0

41
Q

Chain Code
1. find the top-left pixel on the boundary; call P0
2.

A

traverse the four neighborhood of the current pixel in the counter-clockwise order

42
Q

Chain Code
1. find the top-left pixel on the boundary; call P0
2.traverse the four neighborhood of the current pixel in the counter-clockwise order
3

A

stop when current boundary pixel Pk equals to P1 and Pk-1 equals to P0

43
Q

mean shift

1.

A

start from an arbitrary point in the distribution

44
Q

mean shift
1. start from an arbitrary point in the distribution
2.

A

region of interest is a circle centered at this point

45
Q

mean shift
1. start from an arbitrary point in the distribution
2. region of interest is a circle centered at this point
3.

A

on each iteration, find the center of mass for the ROI

46
Q

mean shift
1. start from an arbitrary point in the distribution
2. region of interest is a circle centered at this point
3. on each iteration, find the center of mass for the ROI
4.

A

move the circle to this center

47
Q

mean shift
1. start from an arbitrary point in the distribution
2. region of interest is a circle centered at this point
3. on each iteration, find the center of mass for the ROI
4. move the circle to this center
5.

A

continue the iterations until it convergences

48
Q

hough transform

1.

A

discretize parameter space into bins

49
Q

hough transform

1. discretize parameter space into bins

A

for each feature point in the image, put a vote in every bin in the parameter space that could have generated this point

50
Q

hough transform
1. discretize parameter space into bins
2. for each feature point in the image, put a vote in every bin in the parameter space that could have generated this point
3

A

find bins that have the most votes

51
Q

selecting seed without a-priori knowledge

A

compute the histogram and choose the grey values with the highest peak

52
Q

otsu thresholding method idea

A

find the threshold that minimizes the weighted within-class variance and maximizes the between class variance

53
Q

otsu thresholding method assumption

A

the histogram is a binomial distribution and the objects colors are mostly homogeneous

Programming (15 decks)

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